pymcp-sse
Asynchronous Python library for building Model Context Protocol (MCP) servers and clients over HTTP/SSE, ideal for AI agents, tool integrations, and chatbot ecosystems.
Documentation
pymcp-sse: Python MCP over SSE Library
A lightweight, flexible implementation of the Model Context Protocol (MCP) for Python applications, specializing in robust HTTP/SSE transport.
Features
- Modular Framework: Clean implementation of `BaseMCPServer`, `BaseMCPClient`, and `MultiMCPClient`.
- HTTP/SSE Transport: Robust HTTP/SSE implementation with automatic session management, configurable timeouts, and reconnection handling.
- Concurrent Task Execution: `BaseMCPServer.run_with_tasks()` method for easily running servers with persistent background asynchronous tasks.
- Tool Registration & Discovery: Simple decorator-based tool registration (`@server.register_tool()`) and a standard `describe_tools` endpoint for clients to dynamically query detailed tool capabilities (parameters, descriptions).
- Server Push: Built-in support for server-initiated push notifications to clients and periodic keep-alive pings. Includes `NotificationScheduler` helper class.
- LLM Integration: Includes `BaseLLMClient` abstraction for easy integration with various LLM providers (an Anthropic Claude example is provided).
- Flexible Logging: Configurable logging via `pymcp_sse.utils`.
Installation
To install the library locally for development:
# Navigate to the directory containing pyproject.toml
cd /path/to/your/pymcp-sse
# Install in editable mode
pip install -e .(Once published, installation via `pip install pymcp-sse` will be available.)
Basic Usage
Creating an MCP Server (Simple)
from pymcp_sse.server import BaseMCPServer
from pymcp_sse.utils import configure_logging
configure_logging() # Configure logging (optional)
# Create a server instance
server = BaseMCPServer("My Simple Server")
# Register tools using the decorator
# Type hints are used by describe_tools
@server.register_tool("echo")
async def echo_tool(text: str) -> dict:
'''Echoes the provided text back.'''
return {"response": f"Echo: {text}"}
# Run the server using the standard method
if __name__ == "__main__":
# Additional kwargs are passed to uvicorn.run (e.g., timeout_keep_alive=65)
server.run(host="0.0.0.0", port=8000)Creating an MCP Server (with Background Tasks)
import asyncio
from pymcp_sse.server import BaseMCPServer
from pymcp_sse.utils import configure_logging
configure_logging() # Configure logging (optional)
# Create a server instance
server = BaseMCPServer("My Background Task Server")
# Define your background task
async def my_periodic_task():
while True:
print("Task running...")
await asyncio.sleep(5)
# Define a shutdown callback
async def cleanup():
print("Cleaning up...")
# Run the server using run_with_tasks
async def main():
await server.run_with_tasks(
host="0.0.0.0",
port=8001,
concurrent_tasks=[my_periodic_task],
shutdown_callbacks=[cleanup]
)
if __name__ == "__main__":
asyncio.run(main())Creating a Single Client
import asyncio
from pymcp_sse.client import BaseMCPClient
from pymcp_sse.utils import configure_logging
configure_logging() # Configure logging (optional)
async def main():
# Configure timeouts for stability (read timeout > server ping interval)
client = BaseMCPClient(
"http://localhost:8000", # Point to your server
http_read_timeout=65,
http_connect_timeout=10
)
try:
# Connect and initialize
if await client.connect() and await client.initialize():
print(f"Connected. Available tools: {client.available_tools}")
# Call a tool
result = await client.call_tool("echo", text="Hello, world!")
print(f"Tool Result: {result}")
# Assign a notification handler if needed
# client.notification_handler = your_async_handler
except Exception as e:
print(f"An error occurred: {e}")
finally:
await client.close()
if __name__ == "__main__":
asyncio.run(main())Creating a Multi-Server Client
import asyncio
from pymcp_sse.client import MultiMCPClient
from pymcp_sse.utils import configure_logging
configure_logging() # Configure logging (optional)
async def main():
# Use servers from the examples section
servers = {
"server_basic": "http://localhost:8101",
"server_tasks": "http://localhost:8102"
}
# Configure timeouts for stability (read timeout > server ping interval)
client = MultiMCPClient(
servers,
http_read_timeout=65,
http_connect_timeout=10
)
try:
# Connect to all servers (automatically fetches tool details if describe_tools exists)
connection_results = await client.connect_all()
print(f"Connection Results: {connection_results}")
# Get info about connected servers (including tool details)
server_info = client.get_server_info()
print("\nServer Info:")
for name, info in server_info.items():
print(f"- {name}: Status={info['status']}, Tools={len(info.get('available_tools', []))}, Details Fetched={bool(info.get('tool_details'))}")
# Call a tool on a specific server
if server_info.get("server_basic", {}).get("status") == "connected":
result = await client.call_tool("server_basic", "echo", text="Hello from MultiClient!")
print(f"\nServer Basic Echo Result: {result}")
except Exception as e:
print(f"An error occurred: {e}")
finally:
await client.close()
if __name__ == "__main__":
asyncio.run(main())Documentation
For more detailed usage instructions, notes on the HTTP/SSE implementation, guides on LLM integration, and the protocol specification, please refer to the documentation in the `docs/` directory.
Examples
See the `examples/` directory for complete working examples, including:
- `server_basic`: Demonstrates a simple server using `server.run()`.
- `server_tasks`: Demonstrates a server with background tasks (notification scheduler) using `server.run_with_tasks()`.
- `client`: A multi-server client using `MultiMCPClient` and an `LLMAgent` to interact with both servers via natural language. Requires an API key (set `ANTHROPIC_API_KEY` in a `.env` file in the project root).
- `run_all.py`: A launcher script to easily start `server_basic`, `server_tasks`, and the `client` simultaneously.
- `notification_listener.py`: A simple standalone client for receiving push notifications from any compatible server.
License
MIT
Frequently asked questions
What is pymcp-sse?
pymcp-sse is Asynchronous Python library for building Model Context Protocol (MCP) servers and clients over HTTP/SSE, ideal for AI agents, tool integrations, and chatbot ecosystems.
How do I install pymcp-sse?
Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.
Is pymcp-sse open source?
Yes — it is hosted on GitHub at https://github.com/rvirgilli/pymcp-sse and has 2 stars.
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