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mcp-nlp

@tivaliy

About mcp-nlp

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

Basic information

Category

Other

Runtime

python

Transports

stdio

Publisher

tivaliy

Config

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

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

Tools

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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

FAQ from mcp-nlp

What is MCP-NLP?

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

What are the prerequisites?

Python 3.12, Docker, and uv are required.

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/.

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.

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