Merge pull request #177 from thewtex/mcp-docs

DOC: Add MCP demo and content in main docs

Author: thewtex

docs/index.md

@@ -25,6 +25,7 @@ A lean and kind
 - Writes OME-Zarr v0.4 to v0.5
 - [Sharded Zarr] stores
 - Optional writing via [tensorstore]
+- **Model Context Protocol (MCP) server** for AI agent integration
 
 ```{toctree}
 :maxdepth: 2
@@ -33,6 +34,7 @@ quick_start.md
 installation.md
 python.md
 cli.md
+mcp.md
 itk.md
 methods.md
 development.md

docs/mcp.md

@@ -0,0 +1,96 @@
+# 🤖 MCP Server
+
+The [`ngff-zarr-mcp`] Python package provides a Model Context Protocol (MCP) server that enables seamless integration with AI agents and development tools. This server provides a standardized interface for AI agents to interact with ngff-zarr's capabilities for converting datasets to the OME-Zarr scientific imaging data format.
+
+## What is MCP?
+
+The Model Context Protocol (MCP) is an open standard that allows AI agents to securely access external data sources and tools. It creates a bridge between AI models and various applications, enabling them to work together more effectively.
+
+## Integration with AI Agents
+
+The [`ngff-zarr-mcp`] server can be integrated with various AI agents and development environments, including:
+
+- **GitHub Copilot** in VS Code
+- **OpenCode**
+- **Cursor**
+- **Claude Code**
+- Any other agent that supports the MCP standard
+
+This integration allows you to use natural language to:
+- Convert datasets to OME-Zarr format
+- Optimize compression codecs
+- Configure sharding to limit file count
+- Generate Python scripts for batch processing
+- Validate OME-Zarr stores
+- ... and more!
+
+## Example Usage
+
+Here's an example of how you might interact with the MCP server through an AI agent:
+
+### OpenCode Demo
+
+The `ngff-zarr-mcp` Model Context Protocol (MCP) server converts datasets to the OME-Zarr scientific imaging data format, optimizes the compression codec, ensures a limited number of files are generated, and creates a Python script for re-use.
+
+<script src="https://asciinema.org/a/726628.js" id="asciicast-726628" async="true"></script>
+
+#### Sample Prompts
+
+> Convert LIDCFull.tif to OME-Zarr
+
+> Find the optimal codec to use for this data.
+
+> Use sharding to keep the number of files under 20.
+
+> Create a Python script to convert all files in a provided input directory to a provided output directory. Use the codec we found to be optimal.
+
+## Configuration
+
+The MCP server can be configured for use with different AI agents. Here's an example configuration for OpenCode:
+
+```json
+{
+  "$schema": "https://opencode.ai/config.json",
+  "mcp": {
+    "ngff-zarr": {
+      "type": "local",
+      "command": ["uvx", "ngff-zarr-mcp"],
+      "enabled": true
+    }
+  }
+}
+```
+
+A similar configuration can be used for other agents like GitHub Copilot or Cursor, adjusting the command and environment as needed.
+The [`ngff-zarr-mcp`] provides guidance on how to set up the MCP server for different environments.
+
+## Available Functions
+
+The MCP server provides the following functions:
+
+- **convert_images_to_ome_zarr**: Convert images to OME-Zarr format with customizable parameters
+- **get_ome_zarr_info**: Get detailed information about an OME-Zarr store
+- **validate_ome_zarr_store**: Validate OME-Zarr store structure and metadata
+- **optimize_ome_zarr_store**: Optimize existing stores with new compression/chunking
+
+## Benefits
+
+Using the MCP server provides several advantages:
+
+1. **Natural Language Interface**: Interact with ngff-zarr using conversational prompts
+2. **Automated Workflows**: Let AI agents handle complex conversion tasks
+3. **Intelligent Optimization**: Get AI-driven recommendations for compression and chunking
+4. **Code Generation**: Automatically generate Python scripts for batch processing
+5. **Seamless Integration**: Works within your existing development environment
+
+## Getting Started
+
+To use the MCP server:
+
+1. Install ngff-zarr with MCP support
+2. Configure your AI agent to use the [`ngff-zarr-mcp`] server
+3. Start interacting with natural language prompts
+
+For detailed installation and setup instructions, the [MCP server README](https://github.com/thewtex/ngff-zarr/tree/main/mcp/README.md) for comprehensive setup instructions and examples.
+
+[`ngff-zarr-mcp`]: https://pypi.org/project/ngff-zarr-mcp/

mcp/README.md

@@ -1,8 +1,10 @@
 # ngff-zarr MCP Server
 
-A Model Context Protocol (MCP) server that provides AI agents with the ability
+`ngff-zarr-mcp` is 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.
 
+[![asciicast](https://asciinema.org/a/726628.png)](https://asciinema.org/a/726628)
+
 ## Features
 
 ### Tools

mcp/ngff_zarr_mcp/__about__.py

@@ -1 +1 @@
-__version__ = "0.2.0"
+__version__ = "0.2.1"