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MCP Agent

@Nhahan

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

Nhahan

MCP Agent について

MCP Agent is optimized using the ReWOO pattern, enabling even LLMs with fewer parameters to efficiently utilize MCP Server with minimal token consumption.

MCP Agent の設定

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-agent-nhahan": {
      "command": "python",
      "args": [
        "main.py"
      ]
    }
  }
}

MCP Agent 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 Agent?

MCP Agent is a LangGraph agent based on the ReWOO (Reasoning WithOut Observation) pattern that reduces prompt complexity for small LLMs and integrates with the Model Context Protocol (MCP) server for efficient tool execution.

How to use MCP Agent?

Install dependencies, set environment variables as shown in .env.example, configure MCP servers in mcp.json, then run python main.py. The agent executes the ReWOO workflow through a series of LangGraph nodes.

Key features of MCP Agent

  • Implements the ReWOO pattern to minimize prompt complexity, especially for small LLMs.
  • Filters available tools to those most relevant for the current query.
  • Validates and automatically corrects execution plans on errors.
  • Manages evidence in a dictionary, referencing previous results with placeholders.
  • Orchestrates nine specialized LangGraph nodes for a structured workflow.
  • Designed for efficient interaction with any MCP-compatible server.

Use cases of MCP Agent

  • Automate multi-step reasoning tasks via MCP server tools.
  • Perform complex tool calls using small or resource-constrained language models.
  • Reduce token usage and prompt overloading compared to the ReAct pattern.
  • Validate tool plans before execution to catch errors early.

FAQ from MCP Agent

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コメント

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