mcp-nlp 0.0.1

MCP-NLP is a Python package for Natural Language Processing tasks using the MCP

mcp-nlp

MCP-NLP is a FastMCP application designed to provide NLP (Natural Language Processing) capabilities using the Model Context Protocol (MCP).

• FastMCP Framework v2: A modern framework for fast, Pythonic way to build MCP servers.
• Model Context Protocol (MCP): A protocol that allows for the management and control of LLM contexts.
• NLP-modules
  • textdistance: A module for calculating text distance metrics

Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.12
• Docker
• uv

Installation

1. Clone the repository:

   git clone https://github.com/tivaliy/mcp-nlp.git
   cd mcp-nlp
   

2. Install dependencies (using uv):

   uv sync
   

Configuration

Authentication Modes

The MCP-NLP server supports two authentication modes:

1. Unauthenticated Mode (default):

   • No API key required to access the server
   • Set environment variable API_KEY_ENABLED=False

2. API Key Authentication:

   • Requires a valid API key in the request header
   • Set environment variable API_KEY_ENABLED=True and API_KEY=your-secret-key
   • By default, the header name is X-API-Key (can be customized with API_KEY_NAME)

Example .env file:

# Authentication configuration
API_KEY_ENABLED=True
API_KEY=not-a-secret
# API_KEY_NAME=X-API-Key  # Optional: customize header name

Available Tools

The MCP-NLP server currently provides the following MCP tools:

Text Distance Module

Calculate similarity/distance between text sequences using various algorithms:

• #textdistance_measure:

  • Purpose: Measures text distance between two sequences of strings
  • Parameters:
    • source (required): The source text string
    • reference (required): The reference text string to compare against
    • algorithm (optional): The algorithm to use (default: levenshtein)
    • metric (optional): The metric to use (default: normalized_similarity)
  • Returns: A float value representing the calculated distance/similarity

• #textdistance_list_metrics:

  • Purpose: Lists all supported metrics for text distance algorithms
  • Parameters: None
  • Returns: A list of available metrics: distance, similarity, normalized_distance, normalized_similarity, maximum

• Supported Metrics:

  • distance: Raw distance score
  • similarity: Raw similarity score
  • normalized_distance: Distance normalized to a 0-1 scale
  • normalized_similarity: Similarity normalized to a 0-1 scale (default)
  • maximum: Maximum possible value for the algorithm

• Default Algorithm: Levenshtein

Usage

Local Running

To run the application locally:

1. Start the FastMCP application:

   mcp-nlp --transport streamable-http
   

2. Access the MCP server endpoint at http://127.0.0.1:8000/mcp (in case of streamable-http transport)

Run MCP Server Using Docker

To run the MCP server in a Docker container:

1. Build the Docker image:

   docker build -t mcp-nlp .
   

2. Run the Docker container:

   docker run --rm -e TRANSPORT=streamable-http -p 8000:8000 mcp-nlp
   

3. Access the MCP server endpoint at http://127.0.0.1:8000/mcp (in case of streamable-http transport)

Make sure to set the TRANSPORT environment variable to streamable-http or sse when running the Docker container.

VS Code Integration

To use the MCP-NLP server with VS Code:

1. Make sure your MCP-NLP server is running

2. Add the server configuration to your VS Code settings.json (using stdio transport):

   {
       "servers": {
           "mcp-nlp": {
               "type": "stdio",
               "command": "${workspaceFolder}/.venv/bin/mcp-nlp",
               "env": {
                    "API_KEY_ENABLED": "false"
                }
            }
        }
    }
   

3. Enable MCP in VS Code:

   "chat.mcp.enabled": true,
   "github.copilot.advanced": {
     "mcp.enabled": true
   }
   

4. You can now use the MCP-NLP tools directly in VS Code through GitHub Copilot

MCP | Model Context Protocol | FastMCP | NLP

License

This project is licensed under the MIT License. See the LICENSE file for details.