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Multi Capable Processing MCP Smart Agent

@AdadAlShabab

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

AdadAlShabab

Multi Capable Processing MCP Smart Agent について

It is a modular and extensible AI agentic server system that connects specialized agents through a central REST API. These agents can analyze code repositories, fetch external data (like weather), generate text summaries, and remember past interactions using a persistent memory m

#api#agent#flask#http-server#langchain#mcp-server

Multi Capable Processing MCP Smart 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": {
    "Multi-Capable-Processing-MCP-Smart-Agent": {
      "command": "python",
      "args": [
        "-m",
        "unittest",
        "discover",
        "tests"
      ]
    }
  }
}

Multi Capable Processing MCP Smart 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 Multi Capable Processing MCP Smart Agent?

It is a modular and extensible AI-driven agentic server system that connects specialized agents through a central REST API. The agents can analyze code repositories, fetch external data (like weather), generate text summaries, and remember past interactions using a persistent memory manager. It is designed for rapid prototyping and experimentation with multi-agent workflows.

How to use Multi Capable Processing MCP Smart Agent?

Install dependencies with pip install -r requirements.txt, then start the server by running python server/mcp_server.py. The server exposes POST endpoints for analyzing repositories, getting weather, summarizing text, and retrieving memory. These endpoints can be invoked via curl or integrated into frontends, CLI tools, or other workflows.

Key features of Multi Capable Processing MCP Smart Agent

  • Multi-agent architecture with specialized agents for code, data, and summarization
  • Tool-integrated agents using GitHub API, weather services, and NLP techniques
  • Persistent memory system for contextual recall across tasks
  • RESTful server built with Flask for easy integration
  • Pythonic and fully testable project layout
  • Ready for scaling with OpenAI, LangGraph, or vector databases

Use cases of Multi Capable Processing MCP Smart Agent

  • Analyze a GitHub repository to obtain high-level code metrics like file count
  • Fetch mock weather data for a given location
  • Summarize long blocks of text into concise summaries
  • Serve as a backend for AI-powered assistant or chatbot applications
  • Prototype multi-agent systems before connecting real APIs

よくある質問

1

What is the architecture of the system?

The system uses a multi-agent architecture where each agent (CodeAgent, DataAgent, SummaryAgent) has its own tools and is accessed via a central Flask REST API. A MemoryManager persists outputs for reuse across tasks.

2

What are the runtime requirements?

Python 3.7+ and pip are required. Dependencies are listed in `requirements.txt`.

3

Where does the data live?

Currently, memory is stored in-memory via a simple key-value store. It can be extended to use Redis, vector databases, or other persistent backends.

4

Does it connect to real external APIs?

The current version uses simulated data for GitHub and weather. The README suggests replacing these mocks with real APIs (e.g., GitHub, OpenWeatherMap, LangChain tools) as an expansion.

5

How can I test the server?

Run unit tests with `python -m unittest discover tests`.

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