diff --git a/.github/workflows/mcp-ci.yml b/.github/workflows/mcp-ci.yml
index ed4e155c..4db7e658 100644
--- a/.github/workflows/mcp-ci.yml
+++ b/.github/workflows/mcp-ci.yml
@@ -11,8 +11,8 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.9", "3.10", "3.11", "3.12"]
+        os: [ubuntu-latest, macos-latest]
+        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
 
     steps:
     - uses: actions/checkout@v4
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 7b9fddc1..8a1d54d5 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -9,6 +9,7 @@ repos:
     rev: "v4.6.0"
     hooks:
       - id: check-added-large-files
+        exclude: ^mcp/test/data/input/MR-head\.nrrd$
       - id: check-case-conflict
       - id: check-symlinks
       - id: check-json
diff --git a/mcp/README.md b/mcp/README.md
index 85ef61af..4f98b372 100644
--- a/mcp/README.md
+++ b/mcp/README.md
@@ -1,27 +1,36 @@
 # ngff-zarr MCP Server
 
-A Model Context Protocol (MCP) server that provides AI agents with the ability to convert images to OME-Zarr format using the ngff-zarr library.
+A Model Context Protocol (MCP) server that provides AI agents with the ability
+to convert images to OME-Zarr format using the ngff-zarr library.
 
 ## Features
 
 ### Tools
-- **convert_images_to_ome_zarr**: Convert various image formats to OME-Zarr with full control over metadata, compression, and multiscale generation
-- **get_ome_zarr_info**: Inspect existing OME-Zarr stores and get detailed information
+
+- **convert_images_to_ome_zarr**: Convert various image formats to OME-Zarr with
+  full control over metadata, compression, and multiscale generation
+- **get_ome_zarr_info**: Inspect existing OME-Zarr stores and get detailed
+  information
 - **validate_ome_zarr_store**: Validate OME-Zarr structure and metadata
-- **optimize_ome_zarr_store**: Optimize existing stores with new compression and chunking
+- **optimize_ome_zarr_store**: Optimize existing stores with new compression and
+  chunking
 
 ### Resources
+
 - **supported-formats**: List of supported input/output formats and backends
-- **downsampling-methods**: Available downsampling methods for multiscale generation
+- **downsampling-methods**: Available downsampling methods for multiscale
+  generation
 - **compression-codecs**: Available compression codecs and their characteristics
 
 ### Input Support
+
 - Local files (all formats supported by ngff-zarr)
 - Local directories (Zarr stores)
 - Network URLs (HTTP/HTTPS)
 - S3 URLs (with optional s3fs dependency)
 
 ### Output Optimization
+
 - Multiple compression codecs (gzip, lz4, zstd, blosc variants)
 - Configurable compression levels
 - Flexible chunk sizing
@@ -40,7 +49,7 @@ A Model Context Protocol (MCP) server that provides AI agents with the ability t
 The easiest way to use ngff-zarr MCP server is with `uvx`:
 
 ```bash
-# Install uvx if not already installed  
+# Install uvx if not already installed
 pip install uvx
 
 # Run the MCP server directly from PyPI
@@ -52,9 +61,14 @@ uvx ngff-zarr-mcp
 
 Go to: `Settings` -> `Cursor Settings` -> `MCP` -> `Add new global MCP server`
 
-Pasting the following configuration into your Cursor `~/.cursor/mcp.json` file is the recommended approach. You may also install in a specific project by creating `.cursor/mcp.json` in your project folder. See [Cursor MCP docs](https://docs.cursor.com/context/model-context-protocol) for more info.
+Pasting the following configuration into your Cursor `~/.cursor/mcp.json` file
+is the recommended approach. You may also install in a specific project by
+creating `.cursor/mcp.json` in your project folder. See
+[Cursor MCP docs](https://docs.cursor.com/context/model-context-protocol) for
+more info.
 
 #### Using uvx (recommended)
+
 ```json
 {
   "mcpServers": {
@@ -67,6 +81,7 @@ Pasting the following configuration into your Cursor `~/.cursor/mcp.json` file i
 ```
 
 #### Using direct Python
+
 ```json
 {
   "mcpServers": {
@@ -83,9 +98,11 @@ Pasting the following configuration into your Cursor `~/.cursor/mcp.json` file i
 <details>
 <summary><b>Install in Windsurf</b></summary>
 
-Add this to your Windsurf MCP config file. See [Windsurf MCP docs](https://docs.windsurf.com/windsurf/mcp) for more info.
+Add this to your Windsurf MCP config file. See
+[Windsurf MCP docs](https://docs.windsurf.com/windsurf/mcp) for more info.
 
 #### Using uvx (recommended)
+
 ```json
 {
   "mcpServers": {
@@ -98,6 +115,7 @@ Add this to your Windsurf MCP config file. See [Windsurf MCP docs](https://docs.
 ```
 
 #### SSE Transport
+
 ```json
 {
   "mcpServers": {
@@ -114,9 +132,12 @@ Add this to your Windsurf MCP config file. See [Windsurf MCP docs](https://docs.
 <details>
 <summary><b>Install in VS Code</b></summary>
 
-Add this to your VS Code MCP config file. See [VS Code MCP docs](https://code.visualstudio.com/docs/copilot/chat/mcp-servers) for more info.
+Add this to your VS Code MCP config file. See
+[VS Code MCP docs](https://code.visualstudio.com/docs/copilot/chat/mcp-servers)
+for more info.
 
 #### Using uvx (recommended)
+
 ```json
 "mcp": {
   "servers": {
@@ -129,6 +150,7 @@ Add this to your VS Code MCP config file. See [VS Code MCP docs](https://code.vi
 ```
 
 #### Using pip install
+
 ```json
 "mcp": {
   "servers": {
@@ -142,12 +164,64 @@ Add this to your VS Code MCP config file. See [VS Code MCP docs](https://code.vi
 
 </details>
 
+<details>
+<summary><b>Install in OpenCode</b></summary>
+
+OpenCode is a Go-based CLI application that provides an AI-powered coding
+assistant in the terminal. It supports MCP servers through JSON configuration
+files. See [OpenCode MCP docs](https://opencode.ai/docs/mcp-servers/) for more
+details.
+
+Add this to your OpenCode configuration file (`~/.config/opencode/config.json`
+for global or `opencode.json` for project-specific):
+
+#### Using uvx (recommended)
+
+```json
+{
+  "$schema": "https://opencode.ai/config.json",
+  "mcp": {
+    "ngff-zarr": {
+      "type": "local",
+      "command": ["uvx", "ngff-zarr-mcp"],
+      "enabled": true
+    }
+  }
+}
+```
+
+#### Using pip install
+
+```json
+{
+  "mcp": {
+    "ngff-zarr": {
+      "type": "local",
+      "command": ["python",
+        "-c",
+        "import subprocess; subprocess.run(['pip', 'install', 'ngff-zarr-mcp']); import ngff_zarr_mcp.server; ngff_zarr_mcp.server.main()"
+      ],
+      "enabled": true
+    }
+  }
+}
+```
+
+After adding the configuration, restart OpenCode. The ngff-zarr tools will be
+available in the terminal interface with automatic permission prompts for tool
+execution.
+
+</details>
+
 <details>
 <summary><b>Install in Claude Desktop</b></summary>
 
-Add this to your Claude Desktop `claude_desktop_config.json` file. See [Claude Desktop MCP docs](https://modelcontextprotocol.io/quickstart/user) for more info.
+Add this to your Claude Desktop `claude_desktop_config.json` file. See
+[Claude Desktop MCP docs](https://modelcontextprotocol.io/quickstart/user) for
+more info.
 
 #### Using uvx (recommended)
+
 ```json
 {
   "mcpServers": {
@@ -160,6 +234,7 @@ Add this to your Claude Desktop `claude_desktop_config.json` file. See [Claude D
 ```
 
 #### Using direct installation
+
 ```json
 {
   "mcpServers": {
@@ -175,14 +250,18 @@ Add this to your Claude Desktop `claude_desktop_config.json` file. See [Claude D
 <details>
 <summary><b>Install in Claude Code</b></summary>
 
-Run this command. See [Claude Code MCP docs](https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/tutorials#set-up-model-context-protocol-mcp) for more info.
+Run this command. See
+[Claude Code MCP docs](https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/tutorials#set-up-model-context-protocol-mcp)
+for more info.
 
 #### Using uvx
+
 ```sh
 claude mcp add ngff-zarr -- uvx ngff-zarr-mcp
 ```
 
 #### Using pip
+
 ```sh
 claude mcp add ngff-zarr -- python -m pip install ngff-zarr-mcp && ngff-zarr-mcp
 ```
@@ -192,9 +271,12 @@ claude mcp add ngff-zarr -- python -m pip install ngff-zarr-mcp && ngff-zarr-mcp
 <details>
 <summary><b>Install in Gemini CLI</b></summary>
 
-Add this to your *.gemini/settings.json* Gemini CLI MCP configuration. See the [Gemini CLI configuration docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/configuration.md) for more info.
+Add this to your _.gemini/settings.json_ Gemini CLI MCP configuration. See the
+[Gemini CLI configuration docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/configuration.md)
+for more info.
 
 #### Using uvx (recommended)
+
 ```json
 {
   "mcpServers": {
@@ -216,6 +298,7 @@ Add this to your *.gemini/settings.json* Gemini CLI MCP configuration. See the [
 3. Add a new server with the following configuration:
 
 #### Using uvx (recommended)
+
 ```json
 {
   "mcpServers": {
@@ -232,7 +315,8 @@ Add this to your *.gemini/settings.json* Gemini CLI MCP configuration. See the [
 <details>
 <summary><b>Install in BoltAI</b></summary>
 
-Open the "Settings" page of the app, navigate to "Plugins," and enter the following JSON:
+Open the "Settings" page of the app, navigate to "Plugins," and enter the
+following JSON:
 
 ```json
 {
@@ -245,14 +329,18 @@ Open the "Settings" page of the app, navigate to "Plugins," and enter the follow
 }
 ```
 
-Once saved, you can start using ngff-zarr tools in your conversations. More information is available on [BoltAI's Documentation site](https://docs.boltai.com/docs/plugins/mcp-servers).
+Once saved, you can start using ngff-zarr tools in your conversations. More
+information is available on
+[BoltAI's Documentation site](https://docs.boltai.com/docs/plugins/mcp-servers).
 
 </details>
 
 <details>
 <summary><b>Install in Zed</b></summary>
 
-Add this to your Zed `settings.json`. See [Zed Context Server docs](https://zed.dev/docs/assistant/context-servers) for more info.
+Add this to your Zed `settings.json`. See
+[Zed Context Server docs](https://zed.dev/docs/assistant/context-servers) for
+more info.
 
 ```json
 {
@@ -304,11 +392,15 @@ Add this to your Zed `settings.json`. See [Zed Context Server docs](https://zed.
 <details>
 <summary><b>Install in JetBrains AI Assistant</b></summary>
 
-See [JetBrains AI Assistant Documentation](https://www.jetbrains.com/help/ai-assistant/configure-an-mcp-server.html) for more details.
+See
+[JetBrains AI Assistant Documentation](https://www.jetbrains.com/help/ai-assistant/configure-an-mcp-server.html)
+for more details.
 
-1. In JetBrains IDEs go to `Settings` -> `Tools` -> `AI Assistant` -> `Model Context Protocol (MCP)`
+1. In JetBrains IDEs go to `Settings` -> `Tools` -> `AI Assistant` ->
+   `Model Context Protocol (MCP)`
 2. Click `+ Add`.
-3. Click on `Command` in the top-left corner of the dialog and select the As JSON option from the list
+3. Click on `Command` in the top-left corner of the dialog and select the As
+   JSON option from the list
 4. Add this configuration and click `OK`
 
 ```json
@@ -325,7 +417,9 @@ See [JetBrains AI Assistant Documentation](https://www.jetbrains.com/help/ai-ass
 <details>
 <summary><b>Install in Qodo Gen</b></summary>
 
-See [Qodo Gen docs](https://docs.qodo.ai/qodo-documentation/qodo-gen/qodo-gen-chat/agentic-mode/agentic-tools-mcps) for more details.
+See
+[Qodo Gen docs](https://docs.qodo.ai/qodo-documentation/qodo-gen/qodo-gen-chat/agentic-mode/agentic-tools-mcps)
+for more details.
 
 1. Open Qodo Gen chat panel in VSCode or IntelliJ.
 2. Click Connect more tools.
@@ -344,7 +438,9 @@ See [Qodo Gen docs](https://docs.qodo.ai/qodo-documentation/qodo-gen/qodo-gen-ch
 <details>
 <summary><b>Install in Roo Code</b></summary>
 
-Add this to your Roo Code MCP configuration file. See [Roo Code MCP docs](https://docs.roocode.com/features/mcp/using-mcp-in-roo) for more info.
+Add this to your Roo Code MCP configuration file. See
+[Roo Code MCP docs](https://docs.roocode.com/features/mcp/using-mcp-in-roo) for
+more info.
 
 ```json
 {
@@ -362,7 +458,9 @@ Add this to your Roo Code MCP configuration file. See [Roo Code MCP docs](https:
 <details>
 <summary><b>Install in Amazon Q Developer CLI</b></summary>
 
-Add this to your Amazon Q Developer CLI configuration file. See [Amazon Q Developer CLI docs](https://docs.aws.amazon.com/amazonq/latest/qdeveloper-ug/command-line-mcp-configuration.html) for more details.
+Add this to your Amazon Q Developer CLI configuration file. See
+[Amazon Q Developer CLI docs](https://docs.aws.amazon.com/amazonq/latest/qdeveloper-ug/command-line-mcp-configuration.html)
+for more details.
 
 ```json
 {
@@ -385,7 +483,8 @@ To configure ngff-zarr MCP in Zencoder, follow these steps:
 1. Go to the Zencoder menu (...)
 2. From the dropdown menu, select Agent tools
 3. Click on the Add custom MCP
-4. Add the name and server configuration from below, and make sure to hit the Install button
+4. Add the name and server configuration from below, and make sure to hit the
+   Install button
 
 ```json
 {
@@ -401,7 +500,9 @@ Once the MCP server is added, you can easily continue using it.
 <details>
 <summary><b>Install in Warp</b></summary>
 
-See [Warp Model Context Protocol Documentation](https://docs.warp.dev/knowledge-and-collaboration/mcp#adding-an-mcp-server) for details.
+See
+[Warp Model Context Protocol Documentation](https://docs.warp.dev/knowledge-and-collaboration/mcp#adding-an-mcp-server)
+for details.
 
 1. Navigate `Settings` > `AI` > `Manage MCP servers`.
 2. Add a new MCP server by clicking the `+ Add` button.
@@ -426,6 +527,7 @@ See [Warp Model Context Protocol Documentation](https://docs.warp.dev/knowledge-
 For development work, use pixi (recommended) or pip:
 
 #### Using pixi (Recommended)
+
 ```bash
 # Install pixi if not already installed
 curl -fsSL https://pixi.sh/install.sh | bash
@@ -448,6 +550,7 @@ pixi run typecheck
 ```
 
 #### Using pip
+
 ```bash
 # Clone and install in development mode
 git clone <repository>
@@ -477,7 +580,8 @@ ngff-zarr-mcp --transport sse --host localhost --port 8000
 ### Transport Options
 
 - **STDIO**: Default transport for most MCP clients
-- **SSE**: Server-Sent Events for web-based clients or when HTTP transport is preferred
+- **SSE**: Server-Sent Events for web-based clients or when HTTP transport is
+  preferred
 
 See the installation section above for client-specific configuration examples.
 
@@ -502,7 +606,7 @@ result = await convert_images_to_ome_zarr(
 ```python
 result = await convert_images_to_ome_zarr(
     input_paths=["image.nii.gz"],
-    output_path="brain.ome.zarr", 
+    output_path="brain.ome.zarr",
     dims=["z", "y", "x"],
     scale={"z": 2.0, "y": 0.5, "x": 0.5},
     units={"z": "micrometer", "y": "micrometer", "x": "micrometer"},
@@ -533,11 +637,15 @@ print(f"Dimensions: {info.dimensions}")
 
 ## Examples and Demos
 
-The `examples/` directory contains demonstration scripts and configuration files:
+The `examples/` directory contains demonstration scripts and configuration
+files:
 
-- **[`examples/conversion_demo.py`](examples/conversion_demo.py)** - Interactive demo showing image conversion capabilities
-- **[`examples/structure_demo.py`](examples/structure_demo.py)** - Overview of project structure and available tools
-- **[`examples/README.md`](examples/README.md)** - Detailed information about all examples
+- **[`examples/conversion_demo.py`](examples/conversion_demo.py)** - Interactive
+  demo showing image conversion capabilities
+- **[`examples/structure_demo.py`](examples/structure_demo.py)** - Overview of
+  project structure and available tools
+- **[`examples/README.md`](examples/README.md)** - Detailed information about
+  all examples
 
 Run the demos to explore the server's functionality:
 
@@ -552,27 +660,33 @@ pixi run python examples/structure_demo.py
 ## Supported Formats
 
 ### Input Formats
-- **ITK/ITK-Wasm**: .nii, .nii.gz, .mha, .mhd, .nrrd, .dcm, .jpg, .png, .bmp, etc.
+
+- **ITK/ITK-Wasm**: .nii, .nii.gz, .mha, .mhd, .nrrd, .dcm, .jpg, .png, .bmp,
+  etc.
 - **TIFF**: .tif, .tiff, .svs, .ndpi, .scn (via tifffile)
 - **Video**: .webm, .mp4, .avi, .mov, .gif (via imageio)
 - **Zarr**: .zarr, .ome.zarr
 
 ### Output Formats
+
 - OME-Zarr (.ome.zarr, .zarr)
 
 ## Performance Options
 
 ### Memory Management
+
 - Set memory targets to control RAM usage
 - Use caching for large datasets
 - Configure Dask LocalCluster for distributed processing
 
 ### Compression
+
 - Choose from multiple codecs: gzip, lz4, zstd, blosc variants
 - Adjust compression levels for speed vs. size tradeoffs
 - Use sharding to reduce file count (Zarr v3)
 
 ### Chunking
+
 - Optimize chunk sizes for your access patterns
 - Configure sharding for better performance with cloud storage
 
@@ -580,7 +694,8 @@ pixi run python examples/structure_demo.py
 
 ### Using pixi (Recommended)
 
-Pixi provides reproducible, cross-platform environment management. All Python dependencies are defined in `pyproject.toml` and automatically managed by pixi.
+Pixi provides reproducible, cross-platform environment management. All Python
+dependencies are defined in `pyproject.toml` and automatically managed by pixi.
 
 ```bash
 # Clone and setup environment
@@ -595,21 +710,13 @@ pixi shell -e dev
 pixi run test
 pixi run test-cov
 
-# Lint and format code  
+# Lint and format code
 pixi run lint
 pixi run format
 pixi run typecheck
 
 # Run all checks
 pixi run all-checks
-
-# Build and serve documentation
-pixi run docs
-pixi run docs-build
-
-# Start MCP server for testing
-pixi run dev-server        # STDIO mode
-pixi run dev-server-sse    # SSE mode
 ```
 
 #### Pixi Environments
@@ -644,6 +751,7 @@ ruff check .
 ## Dependencies
 
 ### Core
+
 - mcp: Model Context Protocol implementation
 - ngff-zarr: Core image conversion functionality
 - pydantic: Data validation
@@ -651,6 +759,7 @@ ruff check .
 - aiofiles: Async file operations
 
 ### Optional
+
 - s3fs: S3 storage support
 - gcsfs: Google Cloud Storage support
 - dask[distributed]: Distributed processing
@@ -660,7 +769,8 @@ ruff check .
 <details>
 <summary><b>Python Version Issues</b></summary>
 
-The ngff-zarr-mcp server requires Python 3.9 or higher. If you encounter version errors:
+The ngff-zarr-mcp server requires Python 3.9 or higher. If you encounter version
+errors:
 
 ```bash
 # Check your Python version
diff --git a/mcp/ngff_zarr_mcp/__about__.py b/mcp/ngff_zarr_mcp/__about__.py
index b3f47562..d3ec452c 100644
--- a/mcp/ngff_zarr_mcp/__about__.py
+++ b/mcp/ngff_zarr_mcp/__about__.py
@@ -1 +1 @@
-__version__ = "0.1.2"
+__version__ = "0.2.0"
diff --git a/mcp/ngff_zarr_mcp/server.py b/mcp/ngff_zarr_mcp/server.py
index eb8b00bf..e6225df4 100644
--- a/mcp/ngff_zarr_mcp/server.py
+++ b/mcp/ngff_zarr_mcp/server.py
@@ -174,7 +174,7 @@ async def optimize_ome_zarr_store(
     return await optimize_zarr_store(options)
 
 
-@mcp.resource("supported-formats")
+@mcp.resource("ngff-zarr://supported-formats")
 def get_supported_input_output_formats() -> str:
     """Get information about supported input and output formats."""
     formats = get_supported_formats()
@@ -194,7 +194,7 @@ def get_supported_input_output_formats() -> str:
     return "".join(output)
 
 
-@mcp.resource("downsampling-methods")
+@mcp.resource("ngff-zarr://downsampling-methods")
 def get_downsampling_methods() -> str:
     """Get information about available downsampling methods."""
     methods = get_available_methods()
@@ -219,7 +219,7 @@ def get_downsampling_methods() -> str:
     return "".join(output)
 
 
-@mcp.resource("compression-codecs")
+@mcp.resource("ngff-zarr://compression-codecs")
 def get_compression_codecs() -> str:
     """Get information about available compression codecs."""
     codecs = get_available_compression_codecs()
diff --git a/mcp/ngff_zarr_mcp/tools.py b/mcp/ngff_zarr_mcp/tools.py
index de1d33bd..2280ff1c 100644
--- a/mcp/ngff_zarr_mcp/tools.py
+++ b/mcp/ngff_zarr_mcp/tools.py
@@ -108,29 +108,23 @@ async def convert_to_ome_zarr(
 
                 # Determine if we need to generate multiscales
                 scale_factors = options.scale_factors
+                method = Methods(options.method) if options.method else None
+
+                # Setup caching for large datasets
+                cache = ngff_image.data.nbytes > config.memory_target
+
+                # Always use to_multiscales to create proper Multiscales object
+                # If scale_factors is None, create single-scale multiscales
                 if scale_factors is None:
-                    # Single scale output
-                    multiscales_obj = type(
-                        "Multiscales", (), {"images": [ngff_image]}
-                    )()
-                else:
-                    # Generate multiscales
-                    method = (
-                        Methods(options.method)
-                        if options.method
-                        else Methods.ITKWASM_GAUSSIAN
-                    )
-
-                    # Setup caching for large datasets
-                    cache = ngff_image.data.nbytes > config.memory_target
-
-                    multiscales_obj = to_multiscales(
-                        ngff_image,
-                        scale_factors=scale_factors,
-                        method=method,
-                        chunks=chunks,
-                        cache=cache,
-                    )
+                    scale_factors = []
+
+                multiscales_obj = to_multiscales(
+                    ngff_image,
+                    scale_factors=scale_factors,
+                    method=method,
+                    chunks=chunks,
+                    cache=cache,
+                )
 
                 # Setup output store
                 output_path = Path(options.output_path)
@@ -155,14 +149,41 @@ async def convert_to_ome_zarr(
                         chunks_per_shard = tuple(chunks_per_shard)
 
                 # Convert to OME-Zarr
+                kwargs = {}
+                if options.compression_codec:
+                    # Create proper compressor object
+                    import numcodecs
+
+                    if options.compression_codec == "gzip":
+                        level = options.compression_level or 6
+                        kwargs["compressor"] = numcodecs.GZip(level=level)
+                    elif options.compression_codec == "lz4":
+                        kwargs["compressor"] = numcodecs.LZ4()
+                    elif options.compression_codec == "zstd":
+                        level = options.compression_level or 3
+                        kwargs["compressor"] = numcodecs.Zstd(level=level)
+                    elif options.compression_codec.startswith("blosc"):
+                        # Handle blosc variants like "blosc:lz4", "blosc:zstd", etc.
+                        codec_parts = options.compression_codec.split(":")
+                        if len(codec_parts) == 2:
+                            codec_name = codec_parts[1]
+                        else:
+                            codec_name = "lz4"  # default
+                        level = options.compression_level or 5
+                        kwargs["compressor"] = numcodecs.Blosc(
+                            cname=codec_name, clevel=level
+                        )
+                    else:
+                        # Fallback to string (may work for some codecs)
+                        kwargs["compressor"] = options.compression_codec
+
                 to_ngff_zarr(
                     output_store,
                     multiscales_obj,
                     chunks_per_shard=chunks_per_shard,
                     use_tensorstore=options.use_tensorstore,
                     version=options.ome_zarr_version,
-                    compression_codec=options.compression_codec,
-                    compression_level=options.compression_level,
+                    **kwargs,
                 )
 
                 # Analyze the created store
@@ -171,7 +192,7 @@ async def convert_to_ome_zarr(
                 return ConversionResult(
                     success=True,
                     output_path=str(output_path),
-                    store_info=store_info.dict(),
+                    store_info=store_info.model_dump(),
                     error=None,
                 )
 
@@ -378,7 +399,7 @@ async def optimize_zarr_store(options: OptimizationOptions) -> ConversionResult:
         return ConversionResult(
             success=True,
             output_path=str(output_path),
-            store_info=store_info.dict(),
+            store_info=store_info.model_dump(),
             error=None,
         )
 
diff --git a/mcp/ngff_zarr_mcp/utils.py b/mcp/ngff_zarr_mcp/utils.py
index 9c553baf..a9832cf7 100644
--- a/mcp/ngff_zarr_mcp/utils.py
+++ b/mcp/ngff_zarr_mcp/utils.py
@@ -107,7 +107,8 @@ def analyze_zarr_store(store_path: str) -> StoreInfo:
 
                 store = fsspec.get_mapper(store_path)
         else:
-            store = zarr.DirectoryStore(store_path)  # type: ignore[attr-defined]
+            # For local files, just use the path directly
+            store = store_path
 
         # Load multiscales
         multiscales = from_ngff_zarr(store)
@@ -131,7 +132,16 @@ def analyze_zarr_store(store_path: str) -> StoreInfo:
         version = "0.4"  # Default
         try:
             root = zarr.open(store, mode="r")
-            if "multiscales" in root.attrs:
+
+            # Check for v0.5 format first (ome.version)
+            if "ome" in root.attrs:
+                ome_attrs = root.attrs["ome"]
+                if isinstance(ome_attrs, dict) and "version" in ome_attrs:
+                    version_attr = ome_attrs["version"]
+                    if isinstance(version_attr, str):
+                        version = version_attr
+            # Check for v0.4 format (multiscales[0].version)
+            elif "multiscales" in root.attrs:
                 multiscales_attr = root.attrs["multiscales"]
                 if isinstance(multiscales_attr, list) and len(multiscales_attr) > 0:
                     ms_attrs = multiscales_attr[0]
@@ -206,10 +216,109 @@ def get_supported_formats() -> SupportedFormats:
     input_formats = {
         "ngff_zarr": [".zarr", ".ome.zarr"],
         "zarr": [".zarr"],  # Raw zarr arrays
-        "itkwasm": COMMON_EXTENSIONS + [".aim", ".isq", ".fdf"],  # ITKWasm-specific extensions
+        "itkwasm": COMMON_EXTENSIONS
+        + [".aim", ".isq", ".fdf"],  # ITKWasm-specific extensions
         "itk": COMMON_EXTENSIONS + [],  # ITK-specific extensions
-        "tifffile": [".tif", ".tiff", ".svs", ".ndpi", ".scn"],
-        "imageio": [".webm", ".mp4", ".avi", ".mov", ".gif"],
+        "tifffile": [
+            ".tif",
+            ".tiff",  # Standard TIFF
+            ".ome.tiff",  # OME-TIFF microscopy format
+            ".stk",  # MetaMorph STK format
+            ".gel",  # Molecular Dynamics GEL format
+            ".seq",  # Media Cybernetics SEQ format
+            ".zif",  # Zoomable Image File Format
+            ".qptiff",  # PerkinElmer QPTIFF format
+            ".bif",  # Roche BIF format
+            ".avs",  # Argos AVS format
+            ".dp",  # Philips DP format
+            # Digital pathology formats
+            ".svs",  # Aperio SVS format
+            ".ndpi",  # Hamamatsu NDPI format
+            ".scn",  # Leica SCN format
+            # Note: .lsm is already covered by ITK/ITKWasm in COMMON_EXTENSIONS
+        ],
+        "imageio": [
+            # Video formats (FFMPEG plugin)
+            ".webm",
+            ".mp4",
+            ".avi",
+            ".mov",
+            ".gif",  # Already present
+            ".mkv",
+            ".mpeg",
+            ".mpg",
+            ".wmv",
+            ".flv",
+            ".3gp",
+            ".3g2",
+            ".m4v",
+            ".f4v",
+            ".m2ts",
+            ".vob",
+            ".ogv",
+            ".dv",
+            ".asf",
+            ".rm",
+            ".rmvb",
+            ".ts",
+            ".mts",
+            ".divx",
+            ".xvid",
+            # Image formats not well-supported by ITK/tifffile
+            ".psd",  # Photoshop Document
+            ".pcx",  # ZSoft Paintbrush
+            ".sgi",  # Silicon Graphics Image
+            ".ras",  # Sun Raster
+            ".xbm",
+            ".xpm",  # X Window System
+            ".dds",  # DirectDraw Surface
+            ".cut",  # Dr. Halo
+            ".exr",  # OpenEXR
+            ".ppm",
+            ".pgm",
+            ".pbm",  # Portable Pixmap formats
+            ".webp",  # WebP format
+            ".icns",  # Apple Icon format
+            ".ico",  # Windows Icon format
+            ".tga",  # Targa format
+            ".pict",  # Macintosh PICT
+            ".pcd",  # Kodak PhotoCD
+            # Camera raw formats (FreeImage plugin)
+            ".3fr",
+            ".arw",
+            ".cr2",
+            ".crw",
+            ".nef",
+            ".orf",
+            ".raf",
+            ".rw2",
+            ".dng",
+            ".raw",
+            ".bay",
+            ".cap",
+            ".dcs",
+            ".dcr",
+            ".drf",
+            ".erf",
+            ".fff",
+            ".iiq",
+            ".k25",
+            ".kdc",
+            ".mdc",
+            ".mef",
+            ".mos",
+            ".mrw",
+            ".nrw",
+            ".pef",
+            ".ptx",
+            ".pxn",
+            ".r3d",
+            ".rwl",
+            ".sr2",
+            ".srf",
+            ".srw",
+            ".x3f",
+        ],
     }
 
     output_formats = [".zarr", ".ome.zarr"]
diff --git a/mcp/pixi.lock b/mcp/pixi.lock
index d9a498e1..f41116c7 100644
--- a/mcp/pixi.lock
+++ b/mcp/pixi.lock
@@ -12,6 +12,9 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.6.15-hbd8a1cb_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h1423503_5.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.0-h5888daf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda
@@ -25,10 +28,17 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-asyncio-1.0.0-pyhe01879c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.13.5-hec9711d_102_cp313.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-7_cp313.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8c095d6_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_hd72426e_102.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.1-pyhe01879c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda
       - pypi: https://files.pythonhosted.org/packages/ea/43/ccf9b29669cdb09fd4bfc0a8effeb2973b22a0f3c3be4142d0b485975d11/aiobotocore-2.23.0-py3-none-any.whl
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-      - pypi: https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/01/cf/32f019be5de9f6e180926a50ee5f08648e686c7d9a59f2c5d0806a77b1c7/crc32c-2.7.1-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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-      - pypi: https://files.pythonhosted.org/packages/2c/e1/e6716421ea10d38022b952c159d5161ca1193197fb744506875fbb87ea7b/iniconfig-2.1.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/d9/1e/23647191e7532b7d5ec2475537f8e7ec9baee30477180b0f7805d0161d6c/itk_core-5.4.4.post1-cp311-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/f2/64/677f5be959b799e5c0927ae36780b82e9b81117e3dd508b4c01f33a7234f/itk_filtering-5.4.4.post1-cp311-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
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       - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.6.15-hbd8a1cb_0.conda
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-  requires_python: '>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*'
+- conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda
+  sha256: ab29d57dc70786c1269633ba3dff20288b81664d3ff8d21af995742e2bb03287
+  md5: 962b9857ee8e7018c22f2776ffa0b2d7
+  depends:
+  - python >=3.9
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/colorama?source=hash-mapping
+  size: 27011
+  timestamp: 1733218222191
 - pypi: https://files.pythonhosted.org/packages/27/0f/4b59f7c93b52c2c4ce7387c5a4e135e49891bb3b7408dcc98fe44033bbe0/coverage-7.9.1-cp313-cp313-win_amd64.whl
   name: coverage
   version: 7.9.1
@@ -2869,6 +2952,17 @@ packages:
   - pytest ; extra == 'test'
   - cloudpickle ; extra == 'test'
   requires_python: '>=3.8'
+- conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda
+  sha256: ce61f4f99401a4bd455b89909153b40b9c823276aefcbb06f2044618696009ca
+  md5: 72e42d28960d875c7654614f8b50939a
+  depends:
+  - python >=3.9
+  - typing_extensions >=4.6.0
+  license: MIT and PSF-2.0
+  purls:
+  - pkg:pypi/exceptiongroup?source=compressed-mapping
+  size: 21284
+  timestamp: 1746947398083
 - pypi: https://files.pythonhosted.org/packages/4d/36/2a115987e2d8c300a974597416d9de88f2444426de9571f4b59b2cca3acc/filelock-3.18.0-py3-none-any.whl
   name: filelock
   version: 3.18.0
@@ -3465,11 +3559,17 @@ packages:
   - pytest-enabler>=2.2 ; extra == 'enabler'
   - pytest-mypy ; extra == 'type'
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-  sha256: 9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760
-  requires_python: '>=3.8'
+- conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda
+  sha256: 0ec8f4d02053cd03b0f3e63168316530949484f80e16f5e2fb199a1d117a89ca
+  md5: 6837f3eff7dcea42ecd714ce1ac2b108
+  depends:
+  - python >=3.9
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/iniconfig?source=hash-mapping
+  size: 11474
+  timestamp: 1733223232820
 - pypi: https://files.pythonhosted.org/packages/3a/4e/e5ade29d4478713426f08abb75f871c15a57f7c08ad7a5587c404de583e8/itk_core-5.4.4.post1-cp311-abi3-macosx_11_0_arm64.whl
   name: itk-core
   version: 5.4.4.post1
@@ -4436,10 +4536,10 @@ packages:
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   timestamp: 1738196177597
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+- pypi: https://files.pythonhosted.org/packages/ec/84/0fac7104f46c8c0809487b6e973065df95645ab99cb08cf61a9e9dd2aa41/ngff_zarr-0.13.4-py3-none-any.whl
   name: ngff-zarr
-  version: 0.13.3
-  sha256: e83758b46a2124f21510e42840d73b7744a55f01193f435ffcdf600d16f115ff
+  version: 0.13.4
+  sha256: 01eda6eac5c9a5efb9a417799c1c0a958457ab2bc1f2f09f171afe389b5c48d3
   requires_dist:
   - dask[array]
   - importlib-resources
@@ -4499,13 +4599,13 @@ packages:
   requires_python: '>=3.9'
 - pypi: ./
   name: ngff-zarr-mcp
-  version: 0.1.0
-  sha256: 3fe70de320ca6ac12879f1ee1baaed7c6bef03c84f69c5937eab2a41119e124a
+  version: 0.2.0
+  sha256: 69f7353cc8ace60d0f1bbeedb5b51d6e71df659ea41a8c5e26ebfb525cb4d5fb
   requires_dist:
   - aiofiles
   - httpx
   - mcp
-  - ngff-zarr[cli]
+  - ngff-zarr[cli]>=0.13.4
   - pathvalidate
   - pydantic>=2.0
   - gcsfs ; extra == 'all'
@@ -4686,11 +4786,18 @@ packages:
   version: 5.4.1
   sha256: b5e21d21680bd9ef456885db800c5cb4f76a03879880c0175e1b077fb166fd83
   requires_python: '>=3.8'
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-  name: packaging
-  version: '25.0'
-  sha256: 29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484
-  requires_python: '>=3.8'
+- conda: https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda
+  sha256: 289861ed0c13a15d7bbb408796af4de72c2fe67e2bcb0de98f4c3fce259d7991
+  md5: 58335b26c38bf4a20f399384c33cbcf9
+  depends:
+  - python >=3.8
+  - python
+  license: Apache-2.0
+  license_family: APACHE
+  purls:
+  - pkg:pypi/packaging?source=compressed-mapping
+  size: 62477
+  timestamp: 1745345660407
 - pypi: https://files.pythonhosted.org/packages/05/01/0c8785610e465e4948a01a059562176e4c8088aa257e2e074db868f86d4e/pandas-2.3.0-cp313-cp313-macosx_11_0_arm64.whl
   name: pandas
   version: 2.3.0
@@ -5241,17 +5348,17 @@ packages:
   - pytest>=8.3.4 ; extra == 'test'
   - mypy>=1.14.1 ; extra == 'type'
   requires_python: '>=3.9'
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-  requires_dist:
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-  - tox ; extra == 'dev'
-  - pytest ; extra == 'testing'
-  - pytest-benchmark ; extra == 'testing'
-  - coverage ; extra == 'testing'
-  requires_python: '>=3.9'
+- conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda
+  sha256: a8eb555eef5063bbb7ba06a379fa7ea714f57d9741fe0efdb9442dbbc2cccbcc
+  md5: 7da7ccd349dbf6487a7778579d2bb971
+  depends:
+  - python >=3.9
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/pluggy?source=compressed-mapping
+  size: 24246
+  timestamp: 1747339794916
 - pypi: https://files.pythonhosted.org/packages/a8/87/77cc11c7a9ea9fd05503def69e3d18605852cd0d4b0d3b8f15bbeb3ef1d1/pooch-1.8.2-py3-none-any.whl
   name: pooch
   version: 1.8.2
@@ -5559,45 +5666,51 @@ packages:
   - tomli>=2.0.1 ; extra == 'toml'
   - pyyaml>=6.0.1 ; extra == 'yaml'
   requires_python: '>=3.9'
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-  name: pygments
-  version: 2.19.2
-  sha256: 86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b
-  requires_dist:
-  - colorama>=0.4.6 ; extra == 'windows-terminal'
-  requires_python: '>=3.8'
-- pypi: https://files.pythonhosted.org/packages/29/16/c8a903f4c4dffe7a12843191437d7cd8e32751d5de349d45d3fe69544e87/pytest-8.4.1-py3-none-any.whl
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-  sha256: 539c70ba6fcead8e78eebbf1115e8b589e7565830d7d006a8723f19ac8a0afb7
-  requires_dist:
-  - colorama>=0.4 ; sys_platform == 'win32'
-  - exceptiongroup>=1 ; python_full_version < '3.11'
-  - iniconfig>=1
-  - packaging>=20
-  - pluggy>=1.5,<2
-  - pygments>=2.7.2
-  - tomli>=1 ; python_full_version < '3.11'
-  - argcomplete ; extra == 'dev'
-  - attrs>=19.2 ; extra == 'dev'
-  - hypothesis>=3.56 ; extra == 'dev'
-  - mock ; extra == 'dev'
-  - requests ; extra == 'dev'
-  - setuptools ; extra == 'dev'
-  - xmlschema ; extra == 'dev'
-  requires_python: '>=3.9'
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-  requires_dist:
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-  - typing-extensions>=4.12 ; python_full_version < '3.10'
-  - sphinx>=5.3 ; extra == 'docs'
-  - sphinx-rtd-theme>=1 ; extra == 'docs'
-  - coverage>=6.2 ; extra == 'testing'
-  - hypothesis>=5.7.1 ; extra == 'testing'
-  requires_python: '>=3.9'
+- conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda
+  sha256: 5577623b9f6685ece2697c6eb7511b4c9ac5fb607c9babc2646c811b428fd46a
+  md5: 6b6ece66ebcae2d5f326c77ef2c5a066
+  depends:
+  - python >=3.9
+  license: BSD-2-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/pygments?source=compressed-mapping
+  size: 889287
+  timestamp: 1750615908735
+- conda: https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda
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+  md5: a49c2283f24696a7b30367b7346a0144
+  depends:
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+  - exceptiongroup >=1
+  - iniconfig >=1
+  - packaging >=20
+  - pluggy >=1.5,<2
+  - pygments >=2.7.2
+  - python >=3.9
+  - tomli >=1
+  constrains:
+  - pytest-faulthandler >=2
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/pytest?source=hash-mapping
+  size: 276562
+  timestamp: 1750239526127
+- conda: https://conda.anaconda.org/conda-forge/noarch/pytest-asyncio-1.0.0-pyhe01879c_0.conda
+  sha256: ba4d6fef04289e6f0cce3606ed6ddd6a0835c736d9561e5fec2c14313e3e72d2
+  md5: 8d0e343fd65d3323818087455da9c851
+  depends:
+  - pytest >=8.2,<9
+  - python >=3.9
+  - typing_extensions >=4.12
+  - python
+  license: Apache-2.0
+  license_family: APACHE
+  purls:
+  - pkg:pypi/pytest-asyncio?source=hash-mapping
+  size: 38669
+  timestamp: 1748305379549
 - pypi: https://files.pythonhosted.org/packages/bc/16/4ea354101abb1287856baa4af2732be351c7bee728065aed451b678153fd/pytest_cov-6.2.1-py3-none-any.whl
   name: pytest-cov
   version: 6.2.1
@@ -6464,6 +6577,17 @@ packages:
   purls: []
   size: 3466348
   timestamp: 1748388121356
+- conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_1.conda
+  sha256: 18636339a79656962723077df9a56c0ac7b8a864329eb8f847ee3d38495b863e
+  md5: ac944244f1fed2eb49bae07193ae8215
+  depends:
+  - python >=3.9
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/tomli?source=hash-mapping
+  size: 19167
+  timestamp: 1733256819729
 - pypi: https://files.pythonhosted.org/packages/c7/18/c86eb8e0202e32dd3df50d43d7ff9854f8e0603945ff398974c1d91ac1ef/tomli_w-1.2.0-py3-none-any.whl
   name: tomli-w
   version: 1.2.0
@@ -6513,11 +6637,6 @@ packages:
   version: 7.0.0.20250601
   sha256: 0c372e2d1b6529938a080a6ba4a9358e3dfc8526d82fabf40c1ef9325e4ca52e
   requires_python: '>=3.9'
-- pypi: https://files.pythonhosted.org/packages/69/e0/552843e0d356fbb5256d21449fa957fa4eff3bbc135a74a691ee70c7c5da/typing_extensions-4.14.0-py3-none-any.whl
-  name: typing-extensions
-  version: 4.14.0
-  sha256: a1514509136dd0b477638fc68d6a91497af5076466ad0fa6c338e44e359944af
-  requires_python: '>=3.9'
 - pypi: https://files.pythonhosted.org/packages/17/69/cd203477f944c353c31bade965f880aa1061fd6bf05ded0726ca845b6ff7/typing_inspection-0.4.1-py3-none-any.whl
   name: typing-inspection
   version: 0.4.1
@@ -6525,6 +6644,17 @@ packages:
   requires_dist:
   - typing-extensions>=4.12.0
   requires_python: '>=3.9'
+- conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.1-pyhe01879c_0.conda
+  sha256: 4f52390e331ea8b9019b87effaebc4f80c6466d09f68453f52d5cdc2a3e1194f
+  md5: e523f4f1e980ed7a4240d7e27e9ec81f
+  depends:
+  - python >=3.9
+  - python
+  license: PSF-2.0
+  purls:
+  - pkg:pypi/typing-extensions?source=hash-mapping
+  size: 51065
+  timestamp: 1751643513473
 - pypi: https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl
   name: tzdata
   version: '2025.2'
diff --git a/mcp/pyproject.toml b/mcp/pyproject.toml
index 53817279..e97781a0 100644
--- a/mcp/pyproject.toml
+++ b/mcp/pyproject.toml
@@ -31,7 +31,7 @@ classifiers = [
 ]
 dependencies = [
     "mcp",
-    "ngff-zarr[cli]",
+    "ngff-zarr[cli] >= 0.13.4",
     "pydantic >= 2.0",
     "httpx",
     "aiofiles",
@@ -137,6 +137,23 @@ exclude_lines = [
     "if TYPE_CHECKING:",
 ]
 
+[tool.mypy]
+python_version = "3.13"
+# Basic type checking without being too strict
+warn_return_any = false
+warn_unused_configs = false
+disallow_untyped_defs = false
+check_untyped_defs = false
+# Still check for some basic issues
+show_error_codes = true
+pretty = true
+
+[[tool.mypy.overrides]]
+module = [
+    "numcodecs.*",
+]
+ignore_missing_imports = true
+
 [tool.pixi.project]
 channels = ["conda-forge", "pyconda"]
 platforms = ["linux-64", "osx-64", "osx-arm64", "win-64"]
@@ -146,6 +163,8 @@ ngff-zarr-mcp = { path = ".", editable = true }
 
 [tool.pixi.dependencies]
 python = ">=3.9,<3.14"
+pytest = ">=8.4.1,<9"
+pytest-asyncio = ">=1.0.0,<2"
 
 [tool.pixi.feature.dev.pypi-dependencies]
 ngff-zarr-mcp = { path = ".", editable = true, extras = ["dev"] }
diff --git a/mcp/test/data/input/MR-head.nrrd b/mcp/test/data/input/MR-head.nrrd
new file mode 100644
index 00000000..9d632f7f
Binary files /dev/null and b/mcp/test/data/input/MR-head.nrrd differ
diff --git a/mcp/tests/test_convert_to_ome_zarr.py b/mcp/tests/test_convert_to_ome_zarr.py
new file mode 100644
index 00000000..e4535029
--- /dev/null
+++ b/mcp/tests/test_convert_to_ome_zarr.py
@@ -0,0 +1,250 @@
+"""Tests for convert_to_ome_zarr function."""
+
+import pytest
+import tempfile
+import shutil
+from pathlib import Path
+
+from ngff_zarr_mcp.tools import convert_to_ome_zarr
+from ngff_zarr_mcp.models import ConversionOptions
+
+
+@pytest.fixture
+def test_input_file():
+    """Path to the test input file."""
+    return Path(__file__).parent.parent / "test" / "data" / "input" / "MR-head.nrrd"
+
+
+@pytest.fixture
+def temp_output_dir():
+    """Create a temporary directory for output files."""
+    temp_dir = tempfile.mkdtemp()
+    yield temp_dir
+    # Cleanup after test
+    shutil.rmtree(temp_dir, ignore_errors=True)
+
+
+@pytest.mark.asyncio
+async def test_convert_to_ome_zarr_basic(test_input_file, temp_output_dir):
+    """Test basic conversion of MR-head.nrrd to OME-Zarr."""
+    # Verify input file exists
+    assert test_input_file.exists(), f"Test input file not found: {test_input_file}"
+
+    # Setup output path
+    output_path = Path(temp_output_dir) / "mr_head.ome.zarr"
+
+    # Configure conversion options
+    options = ConversionOptions(
+        output_path=str(output_path),
+        ome_zarr_version="0.4",
+        method="itkwasm_gaussian",
+        # Single scale (no multiscale)
+        scale_factors=None,
+        # Use small chunks for testing
+        chunks=64,
+    )
+
+    # Perform conversion
+    result = await convert_to_ome_zarr([str(test_input_file)], options)
+
+    # Verify conversion succeeded
+    assert result.success, f"Conversion failed: {result.error}"
+    assert result.output_path == str(output_path)
+    assert output_path.exists(), "Output OME-Zarr store was not created"
+
+    # Verify store info
+    store_info = result.store_info
+    assert store_info is not None
+    assert store_info.get("num_scales", 0) > 0
+    assert store_info.get("version") == "0.4"
+    assert len(store_info.get("dimensions", [])) > 0
+    assert len(store_info.get("shape", [])) > 0
+
+
+@pytest.mark.asyncio
+async def test_convert_to_ome_zarr_with_multiscale(test_input_file, temp_output_dir):
+    """Test conversion with multiscale generation."""
+    # Verify input file exists
+    assert test_input_file.exists(), f"Test input file not found: {test_input_file}"
+
+    # Setup output path
+    output_path = Path(temp_output_dir) / "mr_head_multiscale.ome.zarr"
+
+    # Configure conversion options with multiscale
+    options = ConversionOptions(
+        output_path=str(output_path),
+        ome_zarr_version="0.4",
+        method="itkwasm_gaussian",
+        # Generate 3 scales with 2x downsampling
+        scale_factors=[2, 2],
+        chunks=64,
+    )
+
+    # Perform conversion
+    result = await convert_to_ome_zarr([str(test_input_file)], options)
+
+    # Verify conversion succeeded
+    assert result.success, f"Conversion failed: {result.error}"
+    assert result.output_path == str(output_path)
+    assert output_path.exists(), "Output OME-Zarr store was not created"
+
+    # Verify multiscale was created
+    store_info = result.store_info
+    assert store_info is not None
+    assert store_info.get("num_scales", 0) >= 2, "Expected at least 2 scales"
+
+
+@pytest.mark.asyncio
+async def test_convert_to_ome_zarr_with_metadata(test_input_file, temp_output_dir):
+    """Test conversion with custom metadata."""
+    # Verify input file exists
+    assert test_input_file.exists(), f"Test input file not found: {test_input_file}"
+
+    # Setup output path
+    output_path = Path(temp_output_dir) / "mr_head_metadata.ome.zarr"
+
+    # Configure conversion options with metadata
+    options = ConversionOptions(
+        output_path=str(output_path),
+        ome_zarr_version="0.4",
+        method="itkwasm_gaussian",
+        name="MR Head Test Image",
+        dims=["z", "y", "x"],
+        scale={"z": 1.25, "y": 0.9375, "x": 0.9375},
+        units={"z": "millimeter", "y": "millimeter", "x": "millimeter"},
+        chunks=64,
+    )
+
+    # Perform conversion
+    result = await convert_to_ome_zarr([str(test_input_file)], options)
+
+    # Verify conversion succeeded
+    assert result.success, f"Conversion failed: {result.error}"
+    assert result.output_path == str(output_path)
+    assert output_path.exists(), "Output OME-Zarr store was not created"
+
+    # Verify metadata was applied
+    store_info = result.store_info
+    assert store_info is not None
+    assert store_info.get("dimensions") == ["z", "y", "x"]
+
+    # Check that scale information was preserved
+    scale_info = store_info.get("scale_info", {})
+    assert "z" in scale_info
+    assert "y" in scale_info
+    assert "x" in scale_info
+
+
+@pytest.mark.asyncio
+async def test_convert_to_ome_zarr_with_compression(test_input_file, temp_output_dir):
+    """Test conversion with different compression settings."""
+    # Verify input file exists
+    assert test_input_file.exists(), f"Test input file not found: {test_input_file}"
+
+    # Setup output path
+    output_path = Path(temp_output_dir) / "mr_head_compressed.ome.zarr"
+
+    # Configure conversion options with compression
+    options = ConversionOptions(
+        output_path=str(output_path),
+        ome_zarr_version="0.4",
+        method="itkwasm_gaussian",
+        compression_codec="gzip",
+        compression_level=6,
+        chunks=64,
+    )
+
+    # Perform conversion
+    result = await convert_to_ome_zarr([str(test_input_file)], options)
+
+    # Verify conversion succeeded
+    assert result.success, f"Conversion failed: {result.error}"
+    assert result.output_path == str(output_path)
+    assert output_path.exists(), "Output OME-Zarr store was not created"
+
+    # Verify compression was applied (if detectable)
+    store_info = result.store_info
+    assert store_info is not None
+    # Note: compression detection depends on implementation details
+
+
+@pytest.mark.asyncio
+async def test_convert_to_ome_zarr_zarr_v05(test_input_file, temp_output_dir):
+    """Test conversion to OME-Zarr v0.5 format."""
+    # Verify input file exists
+    assert test_input_file.exists(), f"Test input file not found: {test_input_file}"
+
+    # Setup output path
+    output_path = Path(temp_output_dir) / "mr_head_v05.ome.zarr"
+
+    # Configure conversion options for v0.5
+    options = ConversionOptions(
+        output_path=str(output_path),
+        ome_zarr_version="0.5",
+        method="itkwasm_gaussian",
+        chunks=64,
+    )
+
+    # Perform conversion
+    result = await convert_to_ome_zarr([str(test_input_file)], options)
+
+    # Verify conversion succeeded
+    assert result.success, f"Conversion failed: {result.error}"
+    assert result.output_path == str(output_path)
+    assert output_path.exists(), "Output OME-Zarr store was not created"
+
+    # Verify version
+    store_info = result.store_info
+    assert store_info is not None
+    assert store_info.get("version") == "0.5"
+
+
+@pytest.mark.asyncio
+async def test_convert_to_ome_zarr_invalid_input():
+    """Test conversion with invalid input file."""
+    # Use a non-existent file
+    invalid_input = "/nonexistent/file.nrrd"
+
+    # Setup output path
+    output_path = "/tmp/should_not_be_created.ome.zarr"
+
+    # Configure conversion options
+    options = ConversionOptions(
+        output_path=output_path,
+        ome_zarr_version="0.4",
+        method="itkwasm_gaussian",
+    )
+
+    # Perform conversion
+    result = await convert_to_ome_zarr([invalid_input], options)
+
+    # Verify conversion failed
+    assert not result.success
+    assert result.error is not None
+    assert len(result.error) > 0
+
+
+@pytest.mark.asyncio
+async def test_convert_to_ome_zarr_invalid_options(test_input_file, temp_output_dir):
+    """Test conversion with invalid options."""
+    # Verify input file exists
+    assert test_input_file.exists(), f"Test input file not found: {test_input_file}"
+
+    # Setup output path
+    output_path = Path(temp_output_dir) / "mr_head_invalid.ome.zarr"
+
+    # Configure conversion options with invalid dimensions
+    options = ConversionOptions(
+        output_path=str(output_path),
+        ome_zarr_version="0.4",
+        method="invalid_method",  # This should cause validation to fail
+        dims=["z", "y", "x"],
+    )
+
+    # Perform conversion
+    result = await convert_to_ome_zarr([str(test_input_file)], options)
+
+    # Verify conversion failed due to validation
+    assert not result.success
+    assert result.error is not None
+    assert "validation" in result.error.lower() or "method" in result.error.lower()