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Saqr-MCP Server

@ahmedhassan456

代码仓库

基本信息

分类

AI 与智能体

许可证

MIT

传输方式

stdio

作者

ahmedhassan456

关于 Saqr-MCP Server

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 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

Saqr-MCP Server 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 🦅 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

常见问题

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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