what is FastAPI MCP Client?
FastAPI MCP Client is a specialized client library designed to interact with FastAPI servers implementing the Model Context Protocol (MCP) over Server-Sent Events (SSE).
how to use FastAPI MCP Client?
To use the FastAPI MCP Client, install it via pip or UV, and then import it in your Python code to create an asynchronous client that can call operations on MCP-enabled FastAPI services.
key features of FastAPI MCP Client?
- Full implementation of the Model Context Protocol (MCP)
- First-class support for Server-Sent Events (SSE) streaming
- Fully async-compatible for high-performance applications
- Seamless session management and error handling
- Type annotations for better IDE integration
use cases of FastAPI MCP Client?
- Interacting with FastAPI services that support MCP for real-time data processing.
- Streaming data from server to client using SSE for applications like live updates.
- Building high-performance applications that require asynchronous communication with APIs.
FAQ from FastAPI MCP Client?
- What is the Model Context Protocol (MCP)?
MCP is a protocol designed for managing context in applications, allowing for efficient communication between clients and servers.
- Is FastAPI MCP Client suitable for production use?
Yes, it is designed for high-performance applications and includes comprehensive error handling.
- How do I run examples provided in the repository?
Clone the repository, set up a virtual environment, install dependencies, and run the example server and client scripts.
FastAPI MCP Client
A specialized client library specifically designed to work with fastapi-mcp servers that implement the Model Context Protocol (MCP) over Server-Sent Events (SSE). This library provides a seamless way to interact with MCP-enabled FastAPI services.
Installation
# Install with pip
pip install fastapi-mcp-client
# Or with UV
uv add fastapi-mcp-client
Quick Start
import asyncio
from fastapi_mcp_client import MCPClient
async def main():
async with MCPClient("http://localhost:8000") as client:
# Call a non-streaming operation
result = await client.call_operation("echo", {"message": "Hello, MCP!"})
print(f"Echo result: {result}")
# Call a streaming operation with SSE
stream = await client.call_operation(
"generate_numbers",
{"count": 5},
stream=True
)
async for event in stream:
print(f"Event: {event}")
asyncio.run(main())
Features
- MCP Protocol Support: Full implementation of the Model Context Protocol
- SSE Streaming: First-class support for Server-Sent Events (SSE) streaming
- Async-First Design: Fully async-compatible for high-performance applications
- Seamless Session Management: Handles MCP session establishment and message passing
- Error Handling: Comprehensive error handling with fallback mechanisms
- Type Annotations: Full type hints for better IDE integration and validation
Running Examples
The repository includes examples to help you get started. Here's how to run them:
1. Clone the Repository
git clone https://github.com/RooseveltAdvisors/fastapi-mcp-client.git
cd fastapi-mcp-client
2. Set Up Environment
# Create and activate virtual environment
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
# Install all dependencies including examples
uv sync --all-extras
3. Start the Example Server
# In one terminal
cd examples/server
python simpler_server.py
This starts a FastAPI server with several MCP-enabled endpoints:
echo
: Returns the message you sendgenerate_numbers
: Streams a sequence of numberssearch_documents
: Simulates a document search with streaming resultscalculate
: Evaluates a simple math expression
4. Run the SSE Client Example
# In another terminal (with virtual env activated)
cd examples
python sse_example.py
Advanced Usage
Custom Client Configuration
from fastapi_mcp_client import MCPClient, MCPClientConfig
config = MCPClientConfig(
base_url="http://localhost:8000",
timeout=60.0,
log_level="DEBUG"
)
async with MCPClient("http://localhost:8000", config=config) as client:
# Call a streaming tool with custom configuration
stream = await client.call_operation(
"generate_numbers",
{"count": 10},
stream=True
)
async for event in stream:
print(f"Received event: {event}")
Understanding the MCP/SSE Flow
sequenceDiagram
participant C as Client
participant S as Server
Note over C,S: Establish SSE Connection
C->>+S: GET /mcp (Accept: text/event-stream)
S-->>-C: 200 OK (Connection Open)
S-->>C: SSE: data: /mcp/messages/?session_id=XXX
Note over C: Parse session_id=XXX
Note over C,S: MCP Initialization
C->>+S: POST /mcp/messages/?session_id=XXX <br> Payload: {method: "initialize", ...}
S-->>-C: 202 Accepted
Note over C,S: MCP Tool Call
C->>+S: POST /mcp/messages/?session_id=XXX <br> Payload: {method: "tools/call", ...}
S-->>-C: 202 Accepted
Note over C,S: Stream Results
S-->>C: SSE: data: {result_part_1}
S-->>C: SSE: data: {result_part_2}
S-->>C: SSE: data: {final_result}
Note over S: (Closes SSE Connection or sends close event)
Examples
Check out the examples directory for more:
- Server Example: A sample FastAPI MCP server
- SSE Example: Advanced streaming with custom configuration
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
Development Setup
-
Clone the repository
git clone https://github.com/RooseveltAdvisors/fastapi-mcp-client.git cd fastapi-mcp-client
-
Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh
-
Create and activate a virtual environment
uv venv source .venv/bin/activate # On Windows: .venv\Scripts\activate
-
Install development dependencies
uv sync --all-extras
-
Run tests
pytest
Pull Request Process
- Create your feature branch (
git checkout -b feature/amazing-feature
) - Make your changes and ensure tests pass
- Commit your changes (
git commit -m 'Add some amazing feature'
) - Push to the branch (
git push origin feature/amazing-feature
) - Open a Pull Request
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments
- Built to work with FastAPI MCP - a FastAPI extension for adding MCP support to your APIs