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MCP Agent

@Nhahan

基本信息

分类

AI 与智能体

传输方式

stdio

作者

Nhahan

关于 MCP Agent

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 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

MCP Agent 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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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