MCP Agent
Enables configuration through environment variables, allowing users to set API keys and other settings
Provides an HTTP API client for accessing the MCP agent's functionality, including web search capabilities
Supports comprehensive testing of the MCP agent's functionality, including mocked API responses
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Agentsearch for latest AI research papers on multimodal learning"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Agent
A production-ready research agent using the Model Context Protocol (MCP), FastAPI, and Serper web search.
Features
Async MCP server with web search tool (Serper API)
FastAPI client for HTTP access
Full test suite with pytest and Serper API mocking
Related MCP server: WebQuest MCP
Setup
Clone the repo
Install dependencies:
pip install -r requirements.txtSet your Serper API key:
Create a
.envfile:SERPER_API_KEY=your_real_serper_api_key REQUEST_TIMEOUT=30 MAX_RESULTS=20 LOG_LEVEL=INFO
Usage
Start FastAPI client (runs MCP server as subprocess):
python -m client.fastapi_clientPOST /search for web search:
{ "query": "search term", "max_results": 10 }GET /health for health check
Testing
Run all tests:
pytest tests/test_e2e.py -vRun integration test (real Serper API):
pytest -m integration tests/test_e2e.py
MIT License
This server cannot be deployed
Maintenance
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