Context MCP Server

Context MCP Server

A Model Context Protocol (MCP) server that provides intelligent context management and web content fetching capabilities. This server enables AI assistants to efficiently store, retrieve, and manage contextual data while also fetching web content for real-time information access.

Features

• 🔍 Smart Content Fetching: Retrieve web content using Jina Reader API with fallback mechanisms
• 🌐 Web Content Processing: Convert HTML to markdown for better AI consumption
• 💾 File Management: Save fetched content to organized file structures
• 🚀 High Performance: Optimized fetching algorithms with intelligent caching
• 🔧 Easy Integration: Standard MCP protocol compatibility with various AI clients

Available Tools

fetch

Fetches content from a URL and returns it as text. This tool attempts to get content using the Jina Reader API first, and falls back to direct HTTP request if that fails.

Arguments:

• url (string, required): The URL to fetch content from
• max_length (integer, optional): Maximum number of characters to return (default: 5000)
• start_index (integer, optional): Start content from this character index (default: 0)
• raw (boolean, optional): Get raw content without markdown conversion (default: false)

Returns:

• The content of the URL as text

Example usage:

fetch_and_save

Fetches content from a URL and saves it to a file. This tool attempts to get content using the Jina Reader API first, and falls back to direct HTTP request if that fails.

Arguments:

• url (string, required): The URL to fetch content from
• file_path (string, optional): The path where to save the file. If not provided, a filename will be automatically generated based on the URL domain and timestamp
• raw (boolean, optional): Get raw content without markdown conversion (default: false)

Returns:

• The path where the file was saved

Example usage:

Or with automatic naming:

Available Prompts

• fetch
  • Fetch a URL and extract its contents as markdown
  • Arguments:
    • url (string, required): URL to fetch

Installation and Usage

Local Development Setup

1. Clone or download the source code:
   git clone https://github.com/LangGPT/context-mcp-server.git cd context-mcp-server
2. Install dependencies using uv:
   uv sync
3. Test the server:
   uv run python -m context_mcp_server --help

Using with Claude Desktop (Local Source)

Add this configuration to your Claude Desktop config file:

Configuration file locations:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%/Claude/claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

Using with VS Code (Local Source)

Add to your VS Code settings or .vscode/mcp.json:

Installation via Package Manager

Using uv (recommended)

When using uv no specific installation is needed. We will use uvx to directly run context-mcp-server:

Using pip

After installation, run it as:

Package Manager Configuration

Claude Desktop with uvx

VS Code with uvx

Configuration

Environment Variables

CONTEXT_DIR

Sets the working directory where files will be saved when using the fetch_and_save tool.

• Default: data
• Priority: CONTEXT_DIR environment variable > default value data

Example:

Command Line Arguments

--user-agent

By default, depending on if the request came from the model (via a tool), or was user initiated (via a prompt), the server will use either the user-agent:

or:

This can be customized by adding the argument --user-agent=YourUserAgent to the args list in the configuration.

--proxy-url

The server can be configured to use a proxy by using the --proxy-url argument.

Development

Setting up Development Environment

1. Install development dependencies:
   uv sync --dev
2. Run linting and type checking:
   uv run ruff check uv run pyright
3. Build the package:
   uv build

Testing

Test the server locally:

With custom work directory:

Use the MCP inspector for debugging:

With custom work directory:

Making Changes

1. Edit the source code in src/context_mcp_server/
2. Test your changes with uv run python -m context_mcp_server
3. Update version in pyproject.toml if needed
4. Run tests and linting

Debugging

You can use the MCP inspector to debug the server:

For local development:

For uvx installations:

Contributing

We encourage contributions to help expand and improve context-mcp-server. Whether you want to add new tools, enhance existing functionality, or improve documentation, your input is valuable.

License

context-mcp-server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.