Model Context Protocol (MCP) Servers

A collection of Model Context Protocol (MCP) servers designed to extend AI assistants with secure and controlled access to external systems and data sources.

Overview

This repository hosts several MCP servers built on the Model Context Protocol framework. Each server enables AI assistants to interact with various external systems:

• File MCP: Access and manipulate files on the local file system
• Google MCP: Manage and interact with Google services (Gmail, Drive, Calendar)
• Web MCP: Search the internet and retrieve online content
• Voice MCP: Generate lifelike text-to-speech audio using advanced voice synthesis

These servers are compatible with MCP-enabled AI tools such as Cursor, Claude Desktop, and other assistants that support the protocol.

Features

• AI Integration: Seamlessly extend AI assistants with file, Google services, web, and voice capabilities
• Modular Architecture: Deploy servers independently or in combination
• Secure Access: Built-in security controls to safeguard sensitive systems
• Rich Functionality: Comprehensive toolsets tailored to each domain
• Progress Reporting: Real-time updates for long-running tasks
• Consistent Interface: Unified API design for ease of integration

Installation

1. Clone the repository.
2. Create and activate a virtual environment:

   uv venv
   source .venv/bin/activate  # On Windows: env\Scripts\activate

3. Install dependencies:

   uv pip install -r requirements.txt
   uv run python -m playwright install

Server Configuration

Each server has its own configuration requirements:

Voice MCP

Powered by Kokoro TTS with multilingual, multi-voice capabilities.

System Dependencies

• Linux:

  sudo apt-get install espeak-ng

• Windows/macOS: Install espeak-ng via your preferred package manager.

Install Python dependencies:

pip install mcp[cli] uv dotenv kokoro>=0.9.2 soundfile sounddevice
pip install misaki[zh,ja]  # for Japanese and Chinese support

Set environment variables:

export VOICE_LANG=us
export VOICE_NAME=af_nicole
export VOICE_SAMPLE_RATE=24000

File MCP

Define root folders accessible by the assistant:

export MCP_ROOT_FOLDERS="/path/to/folder1;/path/to/folder2"

Google MCP

Requires Google API credentials and token:

export GOOGLE_CREDENTIALS_PATH="/path/to/credentials.json"
export GOOGLE_TOKEN_PATH="/path/to/token.pickle"
export ENABLE_GMAIL="true"
export ENABLE_DRIVE="true"
export ENABLE_CALENDAR="true"

Web MCP

Configure search engines and browser automation settings:

# Search Engine API Keys
export TAVILY_API_KEY="your_tavily_api_key"
export GOOGLE_API_KEY="your_google_api_key" 
export GOOGLE_CSE_ID="your_custom_search_engine_id"
export BING_SUBSCRIPTION_KEY="your_bing_api_key"
export BING_SEARCH_URL="your_bing_endpoint_url"

# Search Settings
export SEARCH_ENGINE="tavily"
export MAX_RESULTS="20"

# Browser Automation
export LOAD_JS="true"
export USE_PLAYWRIGHT="true"
export USE_SELENIUM="false"

Integration with AI Applications

These servers can be integrated into any MCP-compatible AI environment.

Unified MCP Configuration

Create an mcp_config.json file with your server configurations:

{
  "mcpServers": {
    "voice-mcp": {
      "command": "uv",
      "args": [
        "voice_server.py",
        "--lang", "us",
        "--voice", "af_nicole",
        "--sample-rate", "24000"
      ],
      "env": {
        // "VOICE_LANG": "us",
        // "VOICE_NAME": "af_nicole",
        // "VOICE_SAMPLE_RATE": "24000"
      }
    },
    "file-mcp": {
      "command": "uv",
      "args": [
        "file_server.py",
        "--roots",
        "/path/to/folder1",
        "/path/to/folder2"
      ]
    },
    "google-mcp": {
      "command": "uv",
      "args": [
        "server.py",
        "--gmail",
        "--drive",
        "--calendar",
        "--credentials",
        "/path/to/credentials.json",
        "--token",
        "/path/to/token.pickle"
      ]
    },
    "web-mcp": {
      "command": "uv",
      "args": [
        "web_server.py",
        "--search-engine",
        "google",
        "--max-results",
        "20",
        "--load-js",
        "--use-playwright"
      ],
      "env": {
        // "TAVILY_API_KEY": "your_api_key_here",
        "USER_AGENT": "Mozilla/5.0...",
        // "SEARCH_ENGINE": "tavily",
        // "MAX_RESULTS": "20",
        // "LOAD_JS": "true",
        // "USE_PLAYWRIGHT": "true"
      }
    }
  }
}

Integration Instructions

• Claude Desktop: Add MCP configurations via the app settings
• Cursor: Configure MCP servers through the built-in settings UI
• Custom Applications: Use the MCP SDK for integration

Available Tools by Server

Voice MCP

• play_audio: Convert text to speech and play it in-memory

File MCP

• list_files: Browse directory contents
• read_file: Read content of files
• get_file_info: Access file metadata
• get_file_mimetype: Detect MIME types
• search_files_by_name: Search by filename
• search_files_by_content: Search by file content

Google MCP

• Gmail:

  • send_email: Send messages with attachments
  • create_draft_email: Prepare drafts for later use
  • list_emails: View inbox or folders
  • search_emails: Find specific emails
  • get_unread_emails: Check unread messages
  • mark_as_read/mark_as_unread: Manage message status
  • delete_email: Remove specific emails
  • batch_delete_emails: Bulk delete emails
  • get_labels: Retrieve available labels

• Drive:

  • list_drive_files: Browse files and folders
  • search_drive_files: Find content in Drive
  • upload_file: Add new files to Drive
  • download_file: Save Drive files locally
  • create_folder: Organize content in folders
  • get_file_permissions: Check sharing settings
  • share_file: Control access to files
  • get_drive_storage_info: Check quota usage

• Calendar:

  • create_meeting: Schedule new events
  • update_meeting: Modify existing events
  • get_meetings_by_date: View day's schedule
  • get_available_time_slots: Find free time
  • invite_to_meeting: Add attendees
  • delete_meeting: Remove events
  • create_bulk_meetings: Schedule multiple events

Web MCP

• search: Perform search queries
• load_url: Fetch content from a URL
• load_sitemap: Extract URLs from sitemaps
• configure_web: Update client settings

Example AI Interactions

With all MCP servers active, assistants can execute natural-language commands like:

"Play a poem about autumn in a French accent"

"Find all documents in my Downloads folder related to budget planning"

"Check my unread emails and summarize the most important ones"

"Search for the latest research on renewable energy and save key findings to a file"

"Schedule a team meeting for tomorrow at 2pm and send calendar invites to my marketing team"

"Upload the quarterly report to my Google Drive and share it with the finance department"

Security Considerations

To ensure secure deployment:

• Use unique accounts per application or context
• Restrict file access to specific directories
• Manage API keys securely using environment variables
• Avoid hardcoding Google credentials in multi-user environments
• Secure web traffic and endpoints as needed
• Regularly audit permissions and access control settings

Troubleshooting

If you encounter issues:

1. Ensure the MCP server processes are running
2. Confirm your AI assistant supports MCP integration
3. Validate the mcp_config.json paths and options
4. Double-check environment variables and credentials
5. Investigate any local firewall or network rules

Contributing

Contributions are welcome! Please submit pull requests to the relevant MCP server directory, following standard GitHub collaboration practices.

License

This project is released under the MIT License. Refer to individual server directories for any specific licensing details.