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mcp-nlp

@tivaliy

mcp-nlp について

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

基本情報

カテゴリ

その他

ランタイム

python

トランスポート

stdio

公開者

tivaliy

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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.

コメント

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