mcp-nlp

Name: mcp-nlp
Author: tivaliy

@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

Basic information

Config

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

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

Tools

No tools detected

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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