mcpy-cli 0.3.6.3

A CLI for the MCP Modelservice Example

🚀 mcpy-cli: Transform Python functions into production-ready MCP services

mcpy-cli is a powerful toolkit designed to simplify creating, running, and deploying Model Context Protocol (MCP) services. It transforms ordinary Python functions into fully-featured MCP tools with automatic schema generation, endpoint creation, and interactive documentation.

✨ Key Features

• 📦 Automatic Function Discovery: Scans Python files and detects functions without additional markup required

• 🚀 Flexible Deployment Options:

  • run command with hot reload, ideal for local development
  • package command with start scripts for production deployment

• 🔄 An MCP of two modes:

  • Composed Mode: All tools under a single endpoint with automatic namespacing
  • Routed Mode: Microservice-style with directory-based routing

• 🌐 Complete JSON-RPC Implementation: Full compliance with MCP protocol specification

• 🎨 Type-Safe by Design: Automatic validation using Python type hints and docstrings

🔥 Quick Start

Installation

# Using pip
pip install mcpy-cli

# Using uv (recommended for faster dependency resolution)
pip install uv
uv pip install mcpy-cli

Building Your First MCP Service

1. Create a Python file with functions:

# math_tools.py
def add(a: float, b: float) -> float:
    """Add two numbers and return the result"""
    return a + b

def multiply(a: float, b: float) -> float:
    """Multiply two numbers and return the result"""
    return a * b

2. Create another file for different functionality:

# text_tools.py
def concatenate(text1: str, text2: str) -> str:
    """Join two text strings together"""
    return text1 + text2
    
def word_count(text: str) -> int:
    """Count words in a text string"""
    return len(text.split())

3. Run in development mode:

# Start development server with auto-reload
mcpy-cli run --source-path ./ --port 8080 --reload True

# Or using uvx without installation
uvx mcpy-cli run --source-path ./ --port 8080 --reload True

4. Test your service:

• Open http://localhost:8080/mcp-server/mcp in your browser using tools like MCP Inspector
  • In Composed mode (default): tool_math_tools_add, tool_text_tools_word_count
  • In Routed mode: Navigate to each module's endpoint

Production Packaging

1. Package your service for deployment:

# Create a deployable package with all dependencies
mcpy-cli package --source-path ./my_project --package-name math-text-tools

# A zip file will be created: math-text-tools.zip

2. Deploy on your server:

# Extract the package
unzip math-text-tools.zip

# Navigate to the project directory
cd math-text-tools/project

# Run the start script (works on Linux/macOS)
chmod +x start.sh  # Make executable if needed
./start.sh

# On Windows, you can use:
# start.bat  # Will be included in the package

3. Deployment Structure: The package contains:

• Your source code in its original structure
• A generated start.sh script with all necessary parameters
• A requirements.txt file with all dependencies
• README files with usage instructions

🥯️ Two MCP service structures

📋 Composed Mode (Default)

Technical Benefits:

• ✅ Single ASGI Application: All tools are handled by one Starlette app
• ✅ Shared Session State: Tools can share state within a session
• ✅ Reduced Resource Overhead: Only one FastMCP instance runs at the server level
• ✅ Automatic Naming Convention: Tools are prefixed with file name (e.g., tool_math_add)
• ✅ Unified Authentication: Apply auth to all tools at once

Best for:

• Applications requiring unified API access
• Tools that work together cooperatively
• Simplified client integration

Usage:

# Using composed mode (default)
mcpy-cli run --source-path ./my_tools --mode composed

# Access: http://localhost:8080/mcp-server/mcp
# Tools: tool_file1_add, tool_file2_calculate, etc.

🔀 Routed Mode

Technical Benefits:

• ✅ True Microservices: Each module runs as an independent MCP server
• ✅ Namespace Isolation: Tools retain original names without prefixing
• ✅ Selective Scaling: Deploy and scale modules independently
• ✅ Independent State: No shared state between different modules
• ✅ Clean URL Hierarchy: Directory structure is directly reflected in URLs

Best for:

• Large projects or enterprise applications
• Modular deployment and management needs
• Team collaboration with different people maintaining different modules
• Independent scaling of specific functionalities

Usage:

# Using routed mode
mcpy-cli run --source-path ./my_tools --mode routed

# Access endpoints:
# http://localhost:8080/math_tools - Math utilities
# http://localhost:8080/text_tools - Text processing
# http://localhost:8080/data_tools - Data manipulation

🏆 Comprehensive Mode Comparison

Feature	Composed Mode	Routed Mode
Architecture	Monolithic	Microservices
URL Structure	/mcp-server/mcp (single endpoint)	/math_tools/mcp, /text_tools/mcp (multiple)
Tool Naming	Prefixed: tool_file_function	Original: function
Session State	Shared across all tools	Isolated per module
Resource Usage	Lower (single FastMCP instance)	Higher (multiple instances)
Use Case	Cohesive, related functionality	Distinct, separate domains

🔄 When to Choose Each Mode

Choose Composed Mode when:

• You want a simple, unified API
• Your tools are logically related
• You need to minimize resource usage
• You prefer simplified deployment
• You have a single team managing all tools

Choose Routed Mode when:

• You need strong module isolation
• Different teams manage different modules
• You want fine-grained scaling control
• Your tools serve distinct domains
• You need independent versioning or deployment

🌐 Deployment Options

Local Development

# Quick development with hot reload
mcpy-cli run --source-path ./my_project --reload True

# Expose on all interfaces (for network testing)
mcpy-cli run --source-path ./my_project --host 0.0.0.0 --port 9000

# With custom server name and service path
mcpy-cli run --source-path ./my_project --mcp-name CustomTools --server-root /api

Containerized Deployment

Create a Dockerfile for your packaged service:

FROM python:3.10-slim

WORKDIR /app

# Copy packaged service contents
COPY my-service/ .

# Install dependencies
RUN pip install --no-cache-dir -r project/requirements.txt

# Default command runs the service
CMD ["/bin/bash", "project/start.sh"]

# Expose service port
EXPOSE 8080

Production Deployment Strategies

1. ASGI Server with Uvicorn/Gunicorn:

   • Your packaged start.sh already uses Uvicorn
   • For production, consider using Gunicorn as a process manager:

   gunicorn -k uvicorn.workers.UvicornWorker -w 4 main:app
   

2. Kubernetes Deployment:

   # Sample Kubernetes deployment
   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: mcp-service
   spec:
     replicas: 3
     # ... other Kubernetes configuration
   

3. Serverless Functions (AWS Lambda, Google Cloud Functions):

   • Use Mangum for AWS Lambda adaptation:

   from mangum import Mangum
   # ... create your MCP application
   handler = Mangum(app)  # Lambda entry point
   

📚 Client Integration

Python Client Examples

Direct HTTP Client (Standard Library)

import json
import urllib.request

def call_mcp_tool(tool_name, params, endpoint="http://localhost:8080/mcp-server/mcp"):
    # Prepare JSON-RPC payload
    payload = {
        "jsonrpc": "2.0",
        "method": tool_name,
        "params": params,
        "id": 1
    }
    
    # Convert to bytes for request
    data = json.dumps(payload).encode('utf-8')
    
    # Create request with proper headers
    req = urllib.request.Request(
        endpoint,
        data=data,
        headers={'Content-Type': 'application/json'}
    )
    
    # Send request and parse response
    with urllib.request.urlopen(req) as response:
        return json.loads(response.read().decode('utf-8'))

# Example usage with composition mode naming
result = call_mcp_tool("tool_math_tools_add", {"a": 10, "b": 5})
print(f"Result: {result['result']}")  # Result: 15

FastMCP Native Client (Async)

import asyncio
from fastmcp import FastMCP

async def main():
    # Connect to the MCP service
    client = FastMCP("http://localhost:8080/mcp-server/mcp")
    
    # List available tools
    tools = await client.list_tools()
    print(f"Available tools: {', '.join(t.id for t in tools)}")
    
    # Call a tool with parameters
    result = await client.call_tool("tool_math_tools_multiply", {"a": 4, "b": 7})
    print(f"4 × 7 = {result}")  # 4 × 7 = 28
    
    # Call another tool with the same client
    result = await client.call_tool("tool_text_tools_concatenate", 
                                   {"text1": "Hello ", "text2": "World!"})
    print(result)  # Hello World!

# Run the async example
asyncio.run(main())

JavaScript/TypeScript Client

async function callMcpTool(toolName: string, params: Record<string, any>) {
  const response = await fetch('http://localhost:8080/mcp-server/mcp', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      jsonrpc: '2.0',
      method: toolName,
      params: params,
      id: 1,
    }),
  });

  return await response.json();
}

// Example usage
const result = await callMcpTool('tool_math_tools_add', { a: 3, b: 7 });
console.log(`The sum is: ${result.result}`);  // The sum is: 10

🧀 Advanced Configuration

1. Service Persistence

mcpy-cli supports service persistence and state recovery through event storage (EventStore). When enabled, the service stores JSON-RPC messages, allowing execution to resume from specific event points after service interruption or restart.

• Implementation: Default uses SQLiteEventStore, saving event data in a local SQLite database file.
• Enabling: Start the service with the --enable-event-store flag.
• Database Path: Specify with --event-store-path parameter. Default is ./mcp_event_store.db.

# Enable event storage with custom database path
mcpy-cli run --source-path ./my_tools --enable-event-store --event-store-path ./my_service_events.db

This feature is particularly useful for MCP services that need to run for extended periods or maintain session state.

2. Caching

To improve performance and reduce redundant computation, the tool provides session-level tool call caching (SessionToolCallCache).

• Mechanism: This in-memory cache stores tool call results within specific user sessions. When the same tool is called again with identical parameters in the same session, results can be returned directly from the cache without re-executing the tool function.
• Use Case: This cache is primarily activated and effective in "stateful JSON response mode".
• Lifecycle: Cache content is bound to the user session and is cleared when the session ends or is cleared.

This mechanism helps optimize response speed for tools that may be frequently called within a session.

⚙️ Configuration

Command Line Options

Common Options (for all commands)

Option	Description	Default
--source-path	Path to Python files/directory	Current directory
--log-level	Logging level (debug, info, warning, error)	info
--functions	Comma-separated specific functions to expose	All discovered functions
--mcp-name	MCP server name	MCPModelService
--server-root	Root path for MCP service group	/mcp-server
--mcp-base	Base path for MCP protocol endpoints	/mcp
--mode	Architecture mode (composed/routed)	composed
--cors-enabled	Enable CORS middleware	True
--cors-allow-origins	Allowed CORS origins (comma-separated)	* (all origins)

Run Command Options

Option	Description	Default
--host	Network interface to bind	127.0.0.1
--port	Service port	8080
--reload	Enable auto-reload for development	False
--workers	Number of worker processes	1
--enable-event-store	Enable SQLite event store for persistence	False
--event-store-path	Path for event store database	./mcp_event_store.db
--stateless-http	Enable stateless HTTP mode	False
--json-response	Use JSON response format instead of SSE	False

Package Command Options

Option	Description	Default
--package-name	Base name for output package	Required (no default)
--package-host	Host to configure in start script	0.0.0.0
--package-port	Port to configure in start script	8080
--package-reload	Enable auto-reload in packaged service	False
--package-workers	Number of workers in packaged service	1
--mw-service	ModelWhale service mode	True

📖 Documentation & Support

• GitHub Repository: https://github.com/modelcontextprotocol/mcpy-cli
• Full Documentation: Complete Documentation
• Architecture Guide: Architecture Design
• Best Practices: Best Practices Guide
• Issue Tracking: Report bugs and request features on GitHub
• Community: Join discussions and get help

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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