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Saqr-MCP Server

@ahmedhassan456

リポジトリ

基本情報

カテゴリ

AI とエージェント

ライセンス

MIT

トランスポート

stdio

作者

ahmedhassan456

Saqr-MCP Server について

Saqr-MCP is a powerful Python application that implements the Model Context Protocol (MCP) to enable advanced AI assistant capabilities. It supports both local models through Ollama and cloud models through Groq, providing a flexible client-server architecture

Saqr-MCP Server 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": {
    "Saqr-MCP": {
      "command": "uv",
      "args": [
        "venv"
      ]
    }
  }
}

Saqr-MCP 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 🦅 Saqr-MCP?

🦅 Saqr-MCP is a Python application that implements the Model Context Protocol (MCP) to enable advanced AI assistant capabilities. It supports both local models through Ollama and cloud models through Groq, providing a flexible client-server architecture with tools for web search, memory management, document generation, and advanced reasoning.

How to use 🦅 Saqr-MCP?

Install dependencies with UV, configure environment variables (API keys for Tavily, Groq, Mem0; Ollama model name), and run python main.py. Type queries in the interactive console; use quit to exit. To use Groq instead of Ollama, modify main.py to import from src.groq_client.

Key features of 🦅 Saqr-MCP

  • Interactive chat interface for querying models
  • Support for local models (Ollama) and cloud models (Groq)
  • Advanced web search capabilities using Tavily API
  • Word document generation from markdown content
  • Comprehensive memory management system using mem0
  • Advanced reasoning and thought process tracking

Use cases of 🦅 Saqr-MCP

  • Perform real-time web searches to retrieve up-to-date information
  • Create and manage persistent memories for context-aware interactions
  • Generate formatted Word documents from markdown content
  • Record and analyze reasoning processes during complex problem-solving
  • Switch between local and cloud models depending on availability or cost

よくある質問

1

What are the prerequisites for using 🦅 Saqr-MCP?

Python 3.11 or higher, Ollama installed for local model usage, and the UV package manager (recommended).

2

What API keys are required and where do I get them?

You need a Tavily API key from app.tavily.com, a Groq API key from console.groq.com, and a Mem0 API key from mem0.ai. Configure them in a `.env` file based on `.env.example`.

3

How do I switch between local and cloud models?

By default the app uses Ollama. To use Groq, edit `main.py` to import `SaqrMCPClient` from `src.groq_client` instead of `src.ollama_client`.

4

What tools are available on the server?

Tools include web_search, word_file_generator, add_memory, get_all_memories, search_memories, think, get_thoughts, clear_thoughts, and get_thought_stats.

5

What is the project structure?

The entry point is `main.py`. The `src/` folder contains `ollama_client.py`, `groq_client.py`, `server.py`, and `logger.py`.

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