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MCP Langchain Server

@sanjeetkumaritoutlook-user

リポジトリ

基本情報

カテゴリ

AI とエージェント

トランスポート

stdio

作者

sanjeetkumaritoutlook-user

MCP Langchain Server について

概要はまだありません

#mcp-langchain#langchain-server#flask-api

MCP Langchain Server の設定

標準の設定はありません

このサーバーの README には解析可能な MCP 設定ブロックが含まれていません。インストール手順はリポジトリをご確認ください。

リポジトリ

MCP Langchain Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

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 Langchain Server?

Mcp Langchain Server is a Flask API that accepts MCP-style { action, params } requests and routes them to a local LLM (via Ollama) for answers. It uses LangChain to interpret actions and run completely free and locally.

How to use Mcp Langchain Server?

Install dependencies (pip install langchain-community langchain langchain-core langchainhub ollama), run python app.py, then send POST requests with { action, params } to the Flask endpoint. Test with Postman and add new functions to mcp_agent.py.

Key features of Mcp Langchain Server

  • Runs entirely free and locally with Ollama
  • Accepts MCP-style { action, params } via Flask API
  • Uses LangChain to interpret and route actions
  • Supports models like gemma:2b, mistral, llama3
  • Low RAM requirements starting at ~2–3 GB
  • Easily extendable with custom functions

Use cases of Mcp Langchain Server

  • Build a private, offline AI assistant using local LLMs
  • Test and prototype MCP-style action routing
  • Integrate with a frontend for local AI chat
  • Add RAG to fetch live news and summarize locally
  • Experiment with different Ollama models for various tasks

よくある質問

1

What dependencies are required to run Mcp Langchain Server?

You need Python, Flask, LangChain (with community modules), and Ollama. Recommended install: `pip install langchain langchain-community langchain-core langchainhub ollama`.

2

How much RAM do I need for local models?

gemma:2b uses ~2–3 GB RAM, mistral ~4 GB, llama3 at least ~6 GB (8–12 GB total system RAM recommended).

3

Where does data live in this server?

All data and processing stay on your local machine—no external services or cloud dependencies.

4

What are the limitations of the LLMs used?

The models have a static knowledge cut-off (e.g., mid-2023) and cannot answer about events after that date. For live data, you must combine with RAG or a News API.

5

What transport or auth does the server use?

The server runs as a plain Flask HTTP API. There is no built-in authentication; it is designed for local development and testing.

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