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# 🚀 MCP Client with Gemini AI
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Welcome! This project features multiple MCP clients integrated with **Google Gemini AI** to execute tasks via the **Model Context Protocol (MCP)** — with and without LangChain.
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## MCP Client Options
This repository includes **four MCP client options** for various use cases:
| Option | Client Script | LangChain | Config Support | Transport | Tutorial |
|--------|-------------------------------|------------|----------------|-----------|----------|
| 1 | `client.py` | ❌ | ❌ | STDIO | [Legacy Client](https://youtu.be/GAPncIfnDwg) |
| 2 | `langchain_mcp_client.py` | ✅ | ❌ | STDIO | [LangChain Client](https://youtu.be/hccNm88bk6w) |
| 3 | `langchain_mcp_client_wconfig.py` | ✅ | ✅ | STDIO | [Multi-Server](https://youtu.be/nCnBWVv2uTA) |
| 4 | `client_sse.py` | ❌ | ❌ | SSE (Loca & Web) | [SSE Client](https://youtu.be/s0YJNcT1XMA) |
If you want to add or reuse MCP Servers, check out [the MCP Servers repo](https://github.com/modelcontextprotocol/servers).
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## ✪ Features
✅ Connects to an MCP server (STDIO or SSE)
✅ Uses **Google Gemini AI** to interpret user prompts
✅ Allows **Gemini to call MCP tools** via server
✅ Executes tool commands and returns results
✅ (Upcoming) Maintains context and history for conversations
---
### Running the MCP Client
Choose the appropriate command for your preferred client:
- **Legacy STDIO** — `uv run client.py path/to/server.py`
- **LangChain STDIO** — `uv run langchain_mcp_client.py path/to/server.py`
- **LangChain Multi-Server STDIO** — `uv run langchain_mcp_client_wconfig.py path/to/config.json`
- **SSE Client** — `uv run client_sse.py sse_server_url`
### Project Structure
```
mcp-client-gemini/
├── client.py # Basic client (STDIO)
├── langchain_mcp_client.py # LangChain + Gemini
├── langchain_mcp_client_wconfig.py # LangChain + config.json (multi-server)
├── client_sse.py # SSE transport client (local or remote)
├── .env # API key environment file
├── README.md # Project documentation
├── requirements.txt # Dependency list
├── .gitignore # Git ignore rules
├── LICENSE # License information
```
### How It Works
1. You send a prompt:
> Create a file named test.txt
2. The prompt is sent to **Google Gemini AI**.
3. Gemini uses available **MCP tools** to determine a response.
4. The selected tool is executed on the **connected server**.
5. The AI returns results and maintains **conversation context** (if supported).
## 🤝 Contributing
At this time, this project does **not accept external code contributions**.
This is to keep licensing simple and avoid any shared copyright.
You're very welcome to:
✅ Report bugs or request features (via GitHub Issues)
✅ Fork the repo and build your own version
✅ Suggest documentation improvements
If you'd like to collaborate in another way, feel free to open a discussion!
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