Legal MCP Server

A Model Context Protocol (MCP) compliant server for legal document analysis using Azure Blob Storage and Anthropic Claude API.

Features

• Document Management: List and retrieve pre-loaded .txt documents from Azure Blob Storage

• Prompt Library: Manage curated prompts for legal document analysis

• Streaming Analysis: Execute document analysis with real-time progress updates via MCP progress notifications

• MCP SSE Transport: Full compliance with MCP specification using Server-Sent Events

Prerequisites

• Node.js 18+

• Azure Storage Account with:

  • documents-texts container (for .txt documents)

  • prompts container (for prompt library JSON)

• Anthropic Claude API key with credits

Setup

1. Clone and install dependencies:

   git clone <your-repo-url> cd Legal-MCP npm install

2. Configure environment variables:

   cp .env.example .env # Edit .env with your actual values

3. Upload initial prompt library to Azure:

   # Using Azure CLI (after setting up connection) az storage blob upload \ --account-name legalmcpstore \ --container-name prompts \ --name "library.json" \ --file ./prompts/library.json \ --connection-string "$AZURE_STORAGE_CONNECTION_STRING"

4. Upload documents to Azure:

   # Upload `.txt` files to the documents-texts container az storage blob upload \ --account-name legalmcpstore \ --container-name documents-texts \ --name "your-document.txt" \ --file ./path/to/your-document.txt \ --connection-string "$AZURE_STORAGE_CONNECTION_STRING"

Running Locally

Development mode (with auto-reload):

Production mode:

The server will start on http://localhost:3000 (or the port specified in PORT env var).

MCP Client Configuration

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):

Note: For SSE transport, you'll need to configure the HTTP endpoint instead. See MCP documentation for SSE client setup.

Testing with MCP Inspector

You can test the server using the MCP Inspector or any MCP-compatible client:

Available Tools

1. list_documents: Returns all available .txt documents

2. list_prompts: Lists available analysis prompts (with optional search)

3. get_prompt: Retrieves full prompt details by ID

4. add_prompt: Adds a new user-contributed prompt

5. execute_analysis: Executes document analysis with streaming progress updates

Project Structure

Deployment to Azure

See the deployment section in the original plan for Azure App Service deployment instructions.

License

ISC