🦅 Saqr-MCP

Name: 🦅 Saqr-MCP
Author: ahmedhassan456

@ahmedhassan456

关于 🦅 Saqr-MCP

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

基本信息

分类

AI 与智能体

许可证

MIT

运行时

python

传输方式

stdio

发布者

ahmedhassan456

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"
      ]
    }
  }
}

工具

未检测到工具

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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

FAQ from 🦅 Saqr-MCP

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).

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.

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.

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.

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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