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MCP Nlp

@tivaliy

Repository

Basic information

Category

Other

Transports

stdio

Author

tivaliy

About MCP Nlp

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

#nlp#mcp#mcp-server#fastmcp

MCP Nlp config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-nlp": {
      "command": "uv",
      "args": [
        "sync"
      ]
    }
  }
}

MCP Nlp tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is mcp-nlp?

MCP-NLP is a FastMCP application built with the FastMCP v2 framework that provides NLP capabilities, specifically text distance metrics via the textdistance module, using the Model Context Protocol (MCP).

How to use mcp-nlp?

Clone the repository, install dependencies with uv sync, then run locally using uvicorn app.main:http_app --reload or build and run a Docker container with docker build -t mcp-nlp . followed by docker run --rm -p 8000:8000 mcp-nlp. The MCP server endpoint is accessible at http://127.0.0.1:8000/mcp/ using the streamable-http transport.

Key features of mcp-nlp

  • Built on the FastMCP v2 framework for Pythonic MCP servers
  • Provides NLP text distance calculations via textdistance
  • Supports local and Docker deployment
  • Uses streamable-http transport
  • Requires Python 3.12, Docker, and uv

Use cases of mcp-nlp

  • Compute text similarity or distance between strings
  • Add NLP functionality to LLM context management pipelines
  • Deploy a lightweight MCP server for text analysis tasks

Frequently asked questions

1

What is MCP-NLP?

MCP-NLP is a FastMCP application that provides NLP capabilities, including text distance metrics, using the Model Context Protocol.

2

What are the prerequisites?

Python 3.12, Docker, and uv are required.

3

How do I run the MCP server locally?

Create a `.env` file, then run `uvicorn app.main:http_app --reload` and access the endpoint at `http://127.0.0.1:8000/mcp/`.

4

How do I run it using Docker?

Build the image with `docker build -t mcp-nlp .` and run the container with `docker run --rm -p 8000:8000 mcp-nlp`.

Comments

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