About Client
Client is an MCP server published by theailanguage in the AI category: mCP Client Implementation using Python, LangGraph and Gemini.
The repository has 120 stars and 36 forks, with the last commit a year ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues before depending on it in production.
Install
git clone https://github.com/theailanguage/mcp_clientThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
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- ✓README presentThe repository ships documentation.
- ✓License declaredUsable under a stated license.
- !Tests presentNo tests found. Behaviour changes may go unnoticed between versions.
- ✓Dependencies pinnedA lockfile pins dependency versions.
- ✓No dynamic code executionSource scan found no eval, dynamic Function, or shell execution.
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README
🚀 MCP Client with Gemini AI
📢 Subscribe to The AI Language on YouTube!
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.
Happy building, and don’t forget to subscribe!
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 |
| 2 | langchain_mcp_client.py |
✅ | ❌ | STDIO | LangChain Client |
| 3 | langchain_mcp_client_wconfig.py |
✅ | ✅ | STDIO | Multi-Server |
| 4 | client_sse.py |
❌ | ❌ | SSE (Loca & Web) | SSE Client |
If you want to add or reuse MCP Servers, check out the MCP Servers repo.
✪ 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
- You send a prompt:
Create a file named test.txt
- The prompt is sent to Google Gemini AI.
- Gemini uses available MCP tools to determine a response.
- The selected tool is executed on the connected server.
- 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!
README fetched from the repository 6 hours ago. The original is authoritative.