chuk-mcp

A Python client for the Model Context Protocol (MCP), an open standard for connecting AI assistants to external data and tools.

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chuk-mcp

A comprehensive Python client implementation for the Model Context Protocol (MCP) - the open standard for connecting AI assistants to external data and tools.

PyPI version Python Version License: MIT uv

🎯 Project Overview

chuk-mcp is a complete Model Context Protocol (MCP) implementation providing both client and server capabilities with a modern, layered architecture. It supports multiple transport protocols, maintains backward compatibility, and implements cutting-edge features including browser-native operation and structured tool outputs.

What is the Model Context Protocol?

The Model Context Protocol (MCP) is an open standard that enables AI applications to securely access external data and tools. Instead of every AI app building custom integrations, MCP provides a universal interface for:

  • 🔧 Tools: Functions AI can call (APIs, file operations, calculations)
  • 📄 Resources: Data sources AI can read (files, databases, web content)
  • 💬 Prompts: Reusable prompt templates with parameters
  • 🎯 Real-time Data: Live information that changes frequently

Key Benefits:

  • Standardized: One protocol for all integrations
  • Secure: User-controlled access to sensitive data
  • Extensible: Easy to add new capabilities
  • Language-Agnostic: Works across different programming languages

Why Use This Client?

chuk-mcp is a production-ready Python implementation that provides:

✅ Comprehensive MCP Protocol Support - Core features including tools, resources, prompts, with advanced features like sampling and completion
✅ Browser-Native Operation - First-of-kind Pyodide/WebAssembly compatibility
✅ Type Safety - Full type annotations with optional Pydantic integration or graceful fallback
✅ Robust Error Handling - Automatic retries, connection recovery, and detailed error reporting
✅ Multi-Transport Architecture - stdio, HTTP, SSE with extensible interface
✅ Version-Aware Features - Automatic protocol negotiation and graceful degradation
✅ Smart Fallback System - Works with or without dependencies
✅ Production Ready - Battle-tested with proper logging, monitoring, and performance optimization
✅ UV Optimized - First-class support for modern Python packaging with UV

🏗️ Architecture Overview

Layer Structure

┌─────────────────────────────────────────┐
│              CLI & Demo Layer           │ ← __main__.py, demos
├─────────────────────────────────────────┤
│             Client/Server API           │ ← High-level abstractions
├─────────────────────────────────────────┤
│            Protocol Layer               │ ← Messages, types, features
├─────────────────────────────────────────┤
│            Transport Layer              │ ← stdio, HTTP, SSE
├─────────────────────────────────────────┤
│             Base Layer                  │ ← Pydantic fallback, config
└─────────────────────────────────────────┘

Benefits of This Architecture:

  • 🔌 Pluggable Transports: Easy to add HTTP, WebSocket, or other transports
  • ♻️ Reusable Protocol Layer: Can be used by servers, proxies, or other tools
  • 🧪 Testable Components: Each layer can be tested independently
  • 📦 Clean Dependencies: Minimal coupling between layers
  • ⚡ Smart Validation: Optional Pydantic with intelligent fallback
chuk_mcp/
├── protocol/           # 🏗️ Shared protocol layer
│   ├── types/         #    Type definitions and validation
│   ├── messages/      #    Feature-organized messaging
│   └── mcp_pydantic_base.py  # Type system foundation with fallback
├── transports/        # 🚀 Transport implementations  
│   ├── stdio/         #    Process-based communication
│   ├── http/          #    Modern streamable HTTP
│   └── sse/           #    Legacy Server-Sent Events
├── client/            # 🔧 High-level client API
└── server/            # 🏭 Server framework

🚀 Key Features

✅ Comprehensive MCP Protocol Support

  • Protocol Versions: 2025-06-18 (current), 2025-03-26, 2024-11-05
  • Core Features: Tools, Resources, Prompts, Logging, Progress, Cancellation
  • Advanced Features: Sampling, Completion, Roots, Elicitation (version-dependent)
  • Structured Output: NEW in 2025-06-18 - tools can return structured data + schemas
  • Version Negotiation: Automatic protocol version detection and fallback

🌐 Multi-Transport Architecture

  • stdio: Process-based communication (always available)
  • HTTP: Modern Streamable HTTP (replaces SSE)
  • SSE: Server-Sent Events (deprecated but supported for compatibility)
  • Extensible: Clean transport interface for future protocols

🧠 Smart Fallback System

  • Pydantic Detection: Auto-detects and uses Pydantic if available
  • Fallback Mode: Complete validation system when Pydantic unavailable
  • Browser Compatible: Works in Pyodide/WebAssembly environments
  • Zero Dependencies: Core functionality works with stdlib only

Installation

Quick Start with UV (Recommended)

UV is the fastest Python package manager. Choose your installation based on your needs:

# 🚀 Minimal installation (uses lightweight fallback validation)
uv add chuk-mcp

# 🔧 With Pydantic validation (recommended for production)
uv add chuk-mcp[pydantic]

# 🌟 Full features (Pydantic + HTTP transport + all extras)
uv add chuk-mcp[full]

# 🛠️ Development installation (includes testing and examples)
uv add chuk-mcp[dev]

Traditional Installation

# Using pip (if UV not available)
pip install chuk-mcp

# With Pydantic support
pip install chuk-mcp[pydantic]

# Full features
pip install chuk-mcp[full]

Installation Options Explained

OptionDependenciesUse CasePerformance
chuk-mcpCore onlyMinimal deployments, testingFast startup, lightweight validation
chuk-mcp[pydantic]+ PydanticProduction use, type safetyEnhanced validation, better errors
chuk-mcp[full]+ All featuresMaximum functionalityFull feature set
chuk-mcp[dev]+ Dev toolsDevelopment, testingAll tools included

💡 Performance Note: The lightweight fallback validation is ~20x slower than Pydantic (0.010ms vs 0.000ms per operation) but still excellent for most use cases. Use [pydantic] for high-throughput applications.

Verify Installation

# Quick test with UV
uv run python -c "import chuk_mcp; print('✅ chuk-mcp installed successfully')"

# Or test full functionality
uv run --with chuk-mcp[pydantic] python -c "
from chuk_mcp.protocol.mcp_pydantic_base import PYDANTIC_AVAILABLE
print(f'✅ Pydantic available: {PYDANTIC_AVAILABLE}')
"

Protocol Compliance

chuk-mcp provides comprehensive compliance with the MCP specification across multiple protocol versions:

📋 Supported Protocol Versions

  • Latest: 2025-06-18 (primary support)
  • Stable: 2025-03-26 (full compatibility)
  • Legacy: 2024-11-05 (backward compatibility)

📊 Protocol Compliance Matrix

Feature Category2024-11-052025-03-262025-06-18Implementation Status
Core Operations
Tools (list/call)✅✅✅✅ Complete
Resources (list/read/subscribe)✅✅✅✅ Complete
Prompts (list/get)✅✅✅✅ Complete
Transport
Stdio✅✅✅✅ Complete
SSE✅⚠️ Deprecated❌ Removed✅ Legacy Support
HTTP Streaming❌✅✅✅ Complete
Advanced Features
Sampling✅✅✅✅ Complete
Completion✅✅✅✅ Complete
Roots✅✅✅✅ Complete
Elicitation❌❌✅✅ Complete
Quality Features
Progress Tracking✅✅✅✅ Complete
Cancellation✅✅✅✅ Complete
Notifications✅✅✅✅ Complete
Batching✅✅❌ Deprecated✅ Legacy Support

Quick Start

Simple Demo

import anyio
from chuk_mcp import stdio_client, StdioServerParameters
from chuk_mcp.protocol.messages import send_initialize

async def main():
    # Demo with minimal echo server (no external dependencies)
    server_params = StdioServerParameters(
        command="python",
        args=["-c", """
import json, sys
init = json.loads(input())
response = {
    "id": init["id"], 
    "result": {
        "serverInfo": {"name": "Demo", "version": "1.0"}, 
        "protocolVersion": "2025-06-18", 
        "capabilities": {}
    }
}
print(json.dumps(response))
        """]
    )
    
    async with stdio_client(server_params) as (read, write):
        result = await send_initialize(read, write)
        print(f"✅ Connected to {result.serverInfo.name}")

if __name__ == "__main__":
    anyio.run(main)

Run with UV:

uv run --with chuk-mcp[pydantic] python demo.py

Basic Usage with Real Server

import anyio
from chuk_mcp import stdio_client, StdioServerParameters
from chuk_mcp.protocol.messages import send_initialize

async def main():
    # Configure connection to an MCP server
    server_params = StdioServerParameters(
        command="uvx",  # Use uvx to run Python tools
        args=["mcp-server-sqlite", "--db-path", "example.db"]
    )
    
    # Connect and initialize
    async with stdio_client(server_params) as (read_stream, write_stream):
        # Initialize the MCP session
        init_result = await send_initialize(read_stream, write_stream)
        
        if init_result:
            print(f"✅ Connected to {init_result.serverInfo.name}")
            print(f"📋 Protocol version: {init_result.protocolVersion}")
        else:
            print("❌ Failed to initialize connection")

anyio.run(main)

Using the CLI

Test server connectivity instantly:

# Test with quickstart demo
uv run examples/quickstart.py

# Run comprehensive demos
uv run examples/e2e_smoke_test_example.py --demo all

# Test specific server configurations
uv run examples/e2e_smoke_test_example.py --smoke

🎯 Innovation Highlights

1. Browser-Native MCP 🌐

  • Pyodide Compatible: Runs completely in browser via WebAssembly
  • Zero Network Dependencies: Works offline in browser
  • Progressive Enhancement: Uses Pydantic when available, fallback otherwise
  • First-of-Kind: First browser-native MCP implementation

2. Version-Aware Features 🔄

  • Automatic Adaptation: Features enable/disable based on protocol version
  • Graceful Degradation: Older servers work with newer clients
  • Forward Compatibility: Ready for future MCP versions

3. Transport Abstraction 🚀

# Same API across all transports
async with stdio_client(stdio_params) as streams:
    response = await send_message(*streams, "ping")

async with http_client(http_params) as streams:  
    response = await send_message(*streams, "ping")  # Same API!

🆕 Latest Features

Structured Tool Output (2025-06-18)

Tools can now return both human-readable text and machine-processable structured data:

# NEW in 2025-06-18: Tools return structured data + schemas
result = await tool_call("analyze_text", {"text": "Hello world"})

# Text summary for humans
print(result.content[0].text)  # "Analyzed 2 words, positive sentiment"

# Structured data for machines  
data = result.structuredContent[0].data
sentiment_score = data["sentiment"]["score"]  # 0.85
word_count = data["statistics"]["word_count"]  # 2

This enables AI assistants to process tool outputs programmatically while still providing clear summaries for users.

Core Concepts

🔧 Tools - Functions AI Can Call

Tools are functions that AI can execute on your behalf. Examples include file operations, API calls, calculations, or any custom logic.

from chuk_mcp.protocol.messages import send_tools_list, send_tools_call

async def explore_tools(read_stream, write_stream):
    # List available tools
    tools_response = await send_tools_list(read_stream, write_stream)
    
    for tool in tools_response.get("tools", []):
        print(f"🔧 {tool['name']}: {tool['description']}")
    
    # Call a specific tool
    result = await send_tools_call(
        read_stream, write_stream,
        name="execute_sql",
        arguments={"query": "SELECT COUNT(*) FROM users"}
    )
    
    print(f"📊 Query result: {result}")

📄 Resources - Data AI Can Access

Resources are data sources like files, database records, API responses, or any URI-addressable content.

from chuk_mcp.protocol.messages import send_resources_list, send_resources_read

async def explore_resources(read_stream, write_stream):
    # Discover available resources
    resources_response = await send_resources_list(read_stream, write_stream)
    
    for resource in resources_response.get("resources", []):
        print(f"📄 {resource['name']} ({resource.get('mimeType', 'unknown')})")
        print(f"   URI: {resource['uri']}")
    
    # Read specific resource content
    if resources_response.get("resources"):
        first_resource = resources_response["resources"][0]
        content = await send_resources_read(read_stream, write_stream, first_resource["uri"])
        
        for item in content.get("contents", []):
            if "text" in item:
                print(f"📖 Content preview: {item['text'][:200]}...")

💬 Prompts - Reusable Templates

Prompts are parameterized templates that help generate consistent, high-quality AI interactions.

from chuk_mcp.protocol.messages import send_prompts_list, send_prompts_get

async def use_prompts(read_stream, write_stream):
    # List available prompt templates
    prompts_response = await send_prompts_list(read_stream, write_stream)
    
    for prompt in prompts_response.get("prompts", []):
        print(f"💬 {prompt['name']}: {prompt['description']}")
    
    # Get a prompt with custom arguments
    prompt_result = await send_prompts_get(
        read_stream, write_stream,
        name="analyze_data",
        arguments={"dataset": "sales_2024", "metric": "revenue"}
    )
    
    # The result contains formatted messages ready for AI
    for message in prompt_result.get("messages", []):
        print(f"🤖 {message['role']}: {message['content']}")

Configuration

Server Configuration

Create a server_config.json file to define your MCP servers:

{
  "mcpServers": {
    "sqlite": {
      "command": "uvx",
      "args": ["mcp-server-sqlite", "--db-path", "database.db"]
    },
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/files"]
    },
    "github": {
      "command": "uvx",
      "args": ["mcp-server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "${GITHUB_TOKEN}"
      }
    },
    "python": {
      "command": "uv",
      "args": ["run", "--with", "mcp-server-python", "mcp-server-python"],
      "env": {
        "PYTHONPATH": "/custom/python/path"
      }
    }
  }
}

Configuration Loading

from chuk_mcp.transports.stdio import stdio_client, StdioServerParameters
from chuk_mcp.protocol.messages import send_initialize

async def connect_configured_server():
    # Load server configuration
    server_params = StdioServerParameters(
        command="uvx",
        args=["mcp-server-sqlite", "--db-path", "database.db"]
    )
    
    async with stdio_client(server_params) as (read_stream, write_stream):
        init_result = await send_initialize(read_stream, write_stream)
        print(f"Connected to configured server: {init_result.serverInfo.name}")

Advanced Features

🎯 Intelligent Sampling

Let servers request AI to generate content on their behalf (with user approval):

from chuk_mcp.protocol.messages.sampling import (
    send_sampling_create_message, 
    create_sampling_message
)

async def ai_content_generation(read_stream, write_stream):
    # Server can request AI to generate content
    messages = [
        create_sampling_message("user", "Explain quantum computing in simple terms")
    ]
    
    result = await send_sampling_create_message(
        read_stream, write_stream,
        messages=messages,
        max_tokens=1000,
        temperature=0.7
    )
    
    print(f"🤖 AI Generated: {result['content']['text']}")

🎯 Argument Completion

Provide intelligent autocompletion for tool arguments:

from chuk_mcp.protocol.messages.completion import (
    send_completion_complete, 
    create_resource_reference, 
    create_argument_info
)

async def smart_completion(read_stream, write_stream):
    # Get completion suggestions for a resource argument
    response = await send_completion_complete(
        read_stream, write_stream,
        ref=create_resource_reference("file:///project/data/"),
        argument=create_argument_info("filename", "sales_202")
    )
    
    completions = response.get("completion", {}).get("values", [])
    print(f"💡 Suggestions: {completions}")

🔄 Multi-Server Orchestration

Connect to multiple servers simultaneously:

from chuk_mcp.transports.stdio import stdio_client, StdioServerParameters
from chuk_mcp.protocol.messages import send_tools_list

async def multi_server_task():
    """Process data using multiple MCP servers."""
    
    servers = [
        StdioServerParameters(command="uvx", args=["mcp-server-sqlite", "--db-path", "data.db"]),
        StdioServerParameters(command="npx", args=["-y", "@modelcontextprotocol/server-filesystem", "/data"]),
    ]
    
    for i, server_params in enumerate(servers):
        async with stdio_client(server_params) as (read_stream, write_stream):
            print(f"Processing with server {i+1}")
            
            # Each server can have different capabilities
            tools = await send_tools_list(read_stream, write_stream)
            print(f"  Available tools: {len(tools.get('tools', []))}")

📡 Real-time Subscriptions

Subscribe to resource changes for live updates:

from chuk_mcp.protocol.messages.resources import send_resources_subscribe

async def live_monitoring(read_stream, write_stream):
    # Subscribe to file changes
    success = await send_resources_subscribe(
        read_stream, write_stream,
        uri="file:///project/logs/app.log"
    )
    
    if success:
        print("📡 Subscribed to log file changes")
        
        # Handle notifications in your message loop
        # (implementation depends on your notification handling)

Error Handling & Resilience

chuk-mcp provides robust error handling with automatic retries:

from chuk_mcp.protocol.messages import RetryableError, NonRetryableError
from chuk_mcp.protocol.messages import send_tools_call

async def resilient_operations(read_stream, write_stream):
    try:
        # Operations automatically retry on transient failures
        result = await send_tools_call(
            read_stream, write_stream,
            name="network_operation",
            arguments={"url": "https://api.example.com/data"},
            timeout=30.0,  # Extended timeout for slow operations
            retries=5      # More retries for critical operations
        )
        
    except RetryableError as e:
        print(f"⚠️ Transient error after retries: {e}")
        # Handle gracefully - maybe try alternative approach
        
    except NonRetryableError as e:
        print(f"❌ Permanent error: {e}")
        # Handle definitively - operation cannot succeed
        
    except Exception as e:
        print(f"🚨 Unexpected error: {e}")
        # Handle unknown errors

🧪 Testing & Demos

Comprehensive Test Suite

  1. Working Smoke Tests: Full E2E validation with real servers
  2. Pyodide Browser Demo: Live browser testing environment
  3. Tools Demo: Structured output feature showcase
  4. Performance Tests: Throughput and latency benchmarks

Demo Applications

  • CLI Tools: Ready-to-use command-line utilities
  • Interactive Explorer: Hands-on tool testing
  • Protocol Validator: Real-time MCP compliance checking
  • Browser Demo: WebAssembly-based MCP in the browser

Testing & Validation

# Quick validation
uv run examples/quickstart.py

# Run comprehensive tests
uv run examples/e2e_smoke_test_example.py --demo all

# Validate installation scenarios
uv run diagnostics/installation_scenarios_diagnostic.py

# Test specific functionality
uv run examples/e2e_smoke_test_example.py --smoke

# Performance benchmarks
uv run examples/e2e_smoke_test_example.py --performance

Available MCP Servers

The MCP ecosystem includes servers for popular services:

🚀 Install with UV (Recommended)

# Popular Python servers
uv tool install mcp-server-sqlite
uv tool install mcp-server-github
uv tool install mcp-server-postgres

# Or run directly without installation
uv run --with mcp-server-sqlite mcp-server-sqlite --db-path data.db

🟢 Node.js Servers

# Use npx for Node.js servers
npx -y @modelcontextprotocol/server-filesystem /path/to/files
npx -y @modelcontextprotocol/server-brave-search

📁 Available Servers

  • 📁 Filesystem: @modelcontextprotocol/server-filesystem
  • 🗄️ SQLite: mcp-server-sqlite
  • 🐙 GitHub: mcp-server-github
  • ☁️ Google Drive: mcp-server-gdrive
  • 🔍 Web Search: mcp-server-brave-search
  • 📊 PostgreSQL: mcp-server-postgres
  • 📈 Analytics: Various data analytics servers
  • 🔧 Custom: Build your own with the MCP SDK

Find more at: MCP Servers Directory

Building MCP Servers

Want to create your own MCP server? Check out:

Performance & Monitoring

chuk-mcp includes built-in performance monitoring:

import logging

# Enable detailed logging for debugging
logging.basicConfig(level=logging.DEBUG)

# Performance is optimized for:
# - Concurrent server connections
# - Efficient message routing  
# - Minimal memory allocation
# - Fast JSON serialization

📈 Performance Characteristics

Benchmarks (from smoke tests):

  • Connection Setup: ~200ms (fast)
  • Request Throughput: >50 req/sec concurrent
  • Memory Usage: Minimal footprint
  • Browser Performance: <2s load time, instant operations

Performance Highlights:

  • 🚀 Fast Startup: < 1 second connection time
  • ⚡ High Throughput: 50+ requests/second per connection
  • 🔄 Concurrent Operations: Full async/await support
  • 💾 Memory Efficient: Minimal overhead per connection

Installation Performance Matrix

InstallationStartup TimeValidation SpeedMemory UsageDependencies
chuk-mcp< 0.5s0.010ms/op15MBCore only
chuk-mcp[pydantic]< 1.0s0.000ms/op25MB+ Pydantic
chuk-mcp[full]< 1.5s0.000ms/op35MBAll features

Intelligent Dependency Management

chuk-mcp includes intelligent dependency handling with graceful fallbacks:

# Check validation backend
from chuk_mcp.protocol.mcp_pydantic_base import PYDANTIC_AVAILABLE

if PYDANTIC_AVAILABLE:
    print("✅ Using Pydantic for enhanced validation")
    print("   • Better error messages")
    print("   • Faster validation (Rust-based)")
    print("   • Advanced type coercion")
else:
    print("📦 Using lightweight fallback validation")
    print("   • Pure Python implementation")
    print("   • No external dependencies")
    print("   • ~20x slower but still fast")

# Force fallback mode for testing
import os
os.environ["MCP_FORCE_FALLBACK"] = "1"

Development

Setup with UV

git clone https://github.com/chrishayuk/chuk-mcp
cd chuk-mcp

# Install with development dependencies
uv sync

# Activate the virtual environment
source .venv/bin/activate  # Linux/Mac
# or .venv\Scripts\activate  # Windows

Traditional Setup

# Alternative setup with pip
pip install -e ".[dev]"

Development Features

# Test with fallback validation
UV_MCP_FORCE_FALLBACK=1 uv run examples/quickstart.py

# Test with different Python versions
uv run --python 3.11 examples/quickstart.py
uv run --python 3.12 examples/quickstart.py

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Add tests for your changes
  4. Ensure all tests pass with uv run diagnostics/installation_scenarios_diagnostic.py
  5. Submit a pull request

UV Integration Features

Project Templates

# Start a new MCP client project
uv init my-mcp-client
cd my-mcp-client

# Add chuk-mcp with dependencies
uv add chuk-mcp[pydantic]

# Add development tools
uv add --dev pytest black isort

# Create example
cat > main.py << 'EOF'
import anyio
from chuk_mcp import stdio_client, StdioServerParameters

async def main():
    # Your MCP client code here
    pass

if __name__ == "__main__":
    anyio.run(main)
EOF

UV Scripts

Add to your pyproject.toml:

[tool.uv]
dev-dependencies = [
    "chuk-mcp[dev]",
]

[project.scripts]
mcp-client = "my_mcp_client:main"

[tool.uv.scripts]
test-mcp = "uv run examples/quickstart.py"
validate = "uv run diagnostics/installation_scenarios_diagnostic.py"

🎖️ Production Readiness

✅ Enterprise Features

  • Error Recovery: Comprehensive error handling and retry logic
  • Logging: Structured logging with configurable levels
  • Monitoring: Built-in health checks and metrics
  • Security: Input validation and safe subprocess handling

✅ Deployment Options

  • Standalone: Direct process execution
  • Containerized: Docker-ready with minimal dependencies
  • Browser: Progressive Web App deployment via Pyodide
  • Cloud: Stateless operation suitable for serverless

✅ Maintenance

  • Documentation: Comprehensive examples and type hints
  • Testing: 100% working test coverage with real scenarios
  • Migration Support: Clear upgrade paths for new features

🚀 Future Roadmap

Near Term

  • Additional Transports: WebSocket, gRPC support
  • Enhanced Tooling: Visual debugger, protocol inspector
  • Performance: Further optimization for high-throughput scenarios

Long Term

  • Protocol Extensions: Custom capability negotiation
  • Distributed MCP: Multi-server orchestration
  • Visual Builder: GUI for MCP server development

Support & Community

License

MIT License - see LICENSE file for details.

Acknowledgments

  • Built on the Model Context Protocol specification
  • Inspired by the official MCP TypeScript SDK
  • Thanks to the MCP community for feedback and contributions
  • Special thanks to the UV team for making Python package management fast and reliable

🏆 Summary

chuk-mcp represents a production-ready, comprehensive MCP implementation that:

  • ✅ Implements comprehensive MCP protocol features including latest 2025-06-18 capabilities
  • ✅ Provides clean, modern APIs with intelligent fallback systems
  • ✅ Supports multiple transports with a unified interface
  • ✅ Works everywhere - server, desktop, and browser environments
  • ✅ Delivers enterprise-grade reliability with comprehensive error handling
  • ✅ Enables innovation through structured outputs and extensible architecture

This implementation sets a new standard for MCP libraries, being both immediately practical for production use and forward-looking for next-generation MCP applications.

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