Unified MCP Server
Click on "Install 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., "@Unified MCP Serveradd 15 and 27"
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.
🚀 Unified MCP Server - One tool server; multiple protocols
A simple server that seamlessly exposes AI tools and resources through multiple protocols: REST API, MCP (Model Context Protocol), and WebSocket connections.
✨ Features
🔌 Triple Protocol Support
REST API: Standard HTTP endpoints for web integration
MCP over HTTP: Model Context Protocol for AI assistants (Claude, etc.)
WebSocket: Real-time bidirectional communication
🎯 Developer Experience
Simple Decorators:
@tool,@resource,@resource_template,@prompt- that's it!Type Safety: Full type hints with mypy support
Async/Await: Native async support throughout
🏗️ Production Ready
Comprehensive Logging: Structured logging with configurable levels
Error Handling: Graceful error responses and recovery
CORS Support: Cross-origin requests handled
Health Checks: Built-in monitoring endpoints
Related MCP server: Simple HTTP MCP Server
📦 Installation
Using uv (Recommended)
# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh
# Create and activate virtual environment
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
# Install the package
uv add unified-mcp-serverUsing pip
pip install unified-mcp-serverDevelopment Installation
# Clone the repository
git clone <repository-url>
cd unified-mcp-server
# Using uv (recommended)
uv sync --dev
# Or using pip
pip install -e ".[dev]"🚀 Quick Start
Basic Example
from unified_server import create_server, tool, resource, prompt
# Define tools with simple decorators
@tool(description="Add two numbers together")
def add(a: int, b: int) -> int:
"""Add two integers and return the result"""
return a + b
@tool(description="Analyze text sentiment")
def analyze_sentiment(text: str) -> dict:
"""Analyze the sentiment of given text"""
# Your sentiment analysis logic here
return {"sentiment": "positive", "confidence": 0.95}
# Define resources (data sources)
@resource(
uri="config://app/settings",
description="Application configuration",
mime_type="application/json"
)
def get_config():
return {
"app_name": "My App",
"version": "1.0.0",
"features": {"ai_enabled": True}
}
# Define prompts for AI interactions
@prompt(description="Code review prompt")
def code_review_prompt(language: str):
return [{
"role": "user",
"content": {
"type": "text",
"text": f"Review this {language} code for best practices"
}
}]
# Create and run server
if __name__ == "__main__":
server = create_server(name="my-server", version="1.0.0")
server.run(host="0.0.0.0", port=8000)📁 Complete Example
See src/tool_server.py for a comprehensive example with:
Multiple tools (math, search, sentiment analysis)
Various resources (config, user data, documentation)
Advanced prompts with parameters
Real file loading
Error handling
🔧 Usage Examples
🌐 REST API
# List all available tools
curl http://localhost:8000/tools
# Execute a tool
curl -X POST http://localhost:8000/tools/add \
-H "Content-Type: application/json" \
-d '{"a": 15, "b": 27}'
# Get all resources
curl http://localhost:8000/resources
# Read a specific resource
curl http://localhost:8000/resources/get_config
# List available prompts
curl http://localhost:8000/prompts
# Generate a prompt
curl -X POST http://localhost:8000/prompts/code_review_prompt \
-H "Content-Type: application/json" \
-d '{"language": "python"}'🤖 MCP Integration
Claude Desktop Configuration
{
"mcpServers": {
"unified-server": {
"command": "npx",
"args": ["@modelcontextprotocol/server-everything", "http://localhost:8000/mcp"]
}
}
}Direct MCP Client
import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
async def main():
# Connect to your unified server
async with stdio_client(StdioServerParameters(
command="python",
args=["-m", "your_server_module"]
)) as (read, write):
async with ClientSession(read, write) as session:
# Initialize the connection
await session.initialize()
# List available tools
tools = await session.list_tools()
print(f"Available tools: {[tool.name for tool in tools.tools]}")
# Call a tool
result = await session.call_tool("add", {"a": 10, "b": 20})
print(f"Result: {result.content}")
asyncio.run(main())🔌 WebSocket Connection
// Connect via WebSocket for real-time communication
const ws = new WebSocket('ws://localhost:8000/ws');
ws.onopen = function() {
// Send tool execution request
ws.send(JSON.stringify({
type: 'tool_call',
tool: 'add',
parameters: { a: 5, b: 3 }
}));
};
ws.onmessage = function(event) {
const response = JSON.parse(event.data);
console.log('Tool result:', response.result);
};📚 Examples
Basic Usage
examples/basic_example.py- Simple tools and resourcesexamples/advanced_example.py- Async functions, complex schemas
Production Example
src/tool_server.py- Full-featured server with:🔧 Tools: Math operations, search, sentiment analysis
📄 Resources: Configuration, user data, documentation
💬 Prompts: System prompts, code review, debugging
📁 File Operations: Loading real files from disk
🏗️ Architecture
src/unified_server/
├── 🏛️ core/ # Core server and registry
│ ├── server.py # Main FastAPI server
│ ├── registry.py # Tool/resource registry
│ └── config.py # Configuration management
├── 🎨 decorators/ # Decorator implementations
│ ├── tool.py # @tool decorator
│ ├── resource.py # @resource decorator
│ ├── resource_template.py # @resource template decorator
│ └── prompt.py # @prompt decorator
├── 🛣️ routes/ # HTTP route handlers
│ ├── tools.py # Tool execution endpoints
│ ├── resources.py # Resource access endpoints
│ ├── prompts.py # Prompt generation endpoints
│ └── mcp.py # MCP protocol endpoints
├── 🔧 handlers/ # Protocol handlers
│ └── mcp_handlers.py # MCP message handling
└── 🛠️ utils/ # Utilities
├── inspection.py # Function signature analysis
└── logging.py # Logging configuration🔍 API Documentation
Once your server is running, visit:
📖 Interactive Docs:
http://localhost:8000/docs(Swagger UI)📋 ReDoc:
http://localhost:8000/redoc(Alternative documentation)🔧 OpenAPI Schema:
http://localhost:8000/openapi.json
🛠️ Development
Setup Development Environment
# Using uv (recommended)
uv sync --dev
source .venv/bin/activate
# Using pip
pip install -e ".[dev]"Running Tests
# Run all tests
pytest
# Run with coverage
pytest --cov=unified_server
# Run specific test file
pytest tests/test_tools.py -vCode Quality
# Format code
black src tests examples
# Lint code
ruff check src tests examples
# Type checking
mypy src
# Run all quality checks
make lint # if using the provided MakefileProject Commands
# Start development server with auto-reload
uv run python src/tool_server.py
# Run basic example
uv run python examples/basic_example.py
# Run advanced example
uv run python examples/advanced_example.py🐳 Docker Support
# Build image
docker build -t unified-mcp-server .
# Run container
docker run -p 8000:8000 unified-mcp-server
# Using docker-compose
docker-compose up🤝 Contributing
Fork the repository
Create a feature branch:
git checkout -b feature/amazing-featureCommit your changes:
git commit -m 'Add amazing feature'Push to the branch:
git push origin feature/amazing-featureOpen a Pull Request
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
🙏 Acknowledgments
FastAPI for the excellent web framework
MCP Protocol for standardizing AI tool interfaces
Pydantic for data validation and serialization
uvicorn for ASGI server implementation
This server cannot be installed
Maintenance
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