├── .gitignore ├── README.md ├── example.py ├── mcp_agent.py ├── pyproject.toml ├── server ├── __init__.py ├── local_file_server.py └── mcp_server.py └── uv.lock /.gitignore: -------------------------------------------------------------------------------- 1 | # Python-generated files 2 | __pycache__/ 3 | *.py[oc] 4 | build/ 5 | dist/ 6 | wheels/ 7 | *.egg-info 8 | 9 | # Virtual environments 10 | .venv 11 | 12 | .cache -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MCP-AG2 Integration Example 2 | 3 | This project demonstrates the integration of the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) with [AutoGen (AG2)](https://github.com/ag2ai/ag2), showcasing a powerful pattern for building modular, tool-enabled AI agents. 4 | 5 | ## Overview 6 | 7 | The example implements three key components: 8 | 9 | 1. **MCP Server**: A process that exposes resources and tools following the MCP specification 10 | 2. **MCPAssistantAgent**: An AutoGen AssistantAgent extension that implements the MCP client interface 11 | 3. **Example Script**: Demonstrates an MCP-enabled agent using LLM capabilities with MCP resources/tools 12 | 13 | ## Setup 14 | 15 | ### Prerequisites 16 | 17 | 1. Install `uv` package manager: 18 | ```bash 19 | # macOS 20 | brew install uv 21 | 22 | # Other platforms 23 | curl -LsSf https://astral.sh/uv/install.sh | sh 24 | ``` 25 | 26 | ### Installation 27 | 28 | ```bash 29 | # Clone the repository 30 | git clone https://github.com/jtanningbed/mcp-ag2-example 31 | cd mcp-ag2-example 32 | 33 | # Install dependencies 34 | uv sync 35 | 36 | # Run the example 37 | uv run example.py 38 | ``` 39 | 40 | ## Key Benefits 41 | 42 | This integration pattern offers several advantages over traditional tool/function calling implementations: 43 | 44 | ### 1. Protocol-Level Interface Abstraction 45 | 46 | - The MCP client interface itself (`read_resource`, `call_tool`, etc.) is exposed to the LLM agent through AG2's tool registration 47 | - Rather than registering individual tools directly with AG2, we register only the core MCP interface methods 48 | - All specific tools (e.g., `write_file`) are proxied through the `call_tool` interface to the MCP server 49 | - This means tools defined on the MCP server don't need any format conversion for different LLMs - they remain in Anthropic schema format 50 | 51 | ### 2. Dynamic Tool Discovery and Model Agnosticism 52 | 53 | - Agents use `list_tools` to discover available server tools 54 | - While the MCP server defines tools using Anthropic's schema format, the LLM never sees these directly 55 | - The LLM only needs to understand how to use `call_tool` with a name and arguments 56 | - No need to convert tool schemas between different LLM formats since they're abstracted behind the MCP interface 57 | 58 | ### 3. Clean Separation of Concerns 59 | 60 | - MCP server handles: 61 | - Tool implementation details 62 | - Tool schema definitions (in Anthropic format) 63 | - Resource management 64 | - Agent only handles: 65 | - Understanding the core MCP interface methods 66 | - Using `call_tool` to proxy specific tool requests to the server 67 | - LLM integration layer only handles: 68 | - Registering MCP interface methods as tools (e.g., `call_tool` in OpenAI format) 69 | - Routing tool calls through the MCP client 70 | 71 | ### AutoGen-Specific Benefits 72 | 73 | When compared to traditional AutoGen tool implementations: 74 | 75 | 1. **Simplified Tool Integration** 76 | - No need to define tool schemas in multiple formats 77 | - Tools are defined once on the MCP server in Anthropic format 78 | - AG2 only needs the `call_tool` interface registered 79 | 80 | 2. **Enhanced Modularity** 81 | - MCP servers can be used by any MCP-compatible client 82 | - Tools and resources are completely decoupled from agent implementation 83 | - New tools can be added to the server without any agent changes 84 | 85 | ## Architecture Overview 86 | 87 | ```mermaid 88 | graph LR 89 | A[LLM] -->|Uses| B[AG2 Tool Interface] 90 | B -->|Registered| C[MCP Interface Methods] 91 | C -->|Proxy| D[MCP Client] 92 | D -->|Protocol| E[MCP Server] 93 | E -->|Implements| F[Tools & Resources] 94 | ``` 95 | -------------------------------------------------------------------------------- /example.py: -------------------------------------------------------------------------------- 1 | """ 2 | Example script demonstrating MCP-enabled AutoGen agent capabilities. 3 | 4 | This script showcases the integration between AutoGen agents and MCP servers, 5 | demonstrating both sequential and parallel operations using tools and resources. 6 | """ 7 | 8 | import asyncio 9 | import os 10 | from mcp_agent import MCPAssistantAgent 11 | from autogen import ConversableAgent, UserProxyAgent 12 | 13 | 14 | async def example(): 15 | """Run an example interaction with an MCP-enabled AutoGen agent.""" 16 | 17 | # Ensure data directory exists 18 | os.makedirs("./data", exist_ok=True) 19 | 20 | # Configure the assistant 21 | assistant = MCPAssistantAgent( 22 | name="mcp_assistant", 23 | system_message=_get_system_message(), 24 | mcp_server_command="uv", 25 | mcp_server_args=["run", "-m", "server.local_file_server"], 26 | llm_config={ 27 | "config_list": [ 28 | { 29 | "model": "gpt-4o", 30 | "api_key": os.environ.get("OPENAI_API_KEY"), 31 | } 32 | ] 33 | }, 34 | code_execution_config={"work_dir": "workspace", "use_docker": False}, 35 | ) 36 | 37 | # Configure the executor 38 | executor = UserProxyAgent( 39 | name="executor", 40 | human_input_mode="NEVER", 41 | code_execution_config={"work_dir": "data", "use_docker": False}, 42 | function_map={ 43 | "read_resource": assistant.read_resource, 44 | "call_tool": assistant.call_tool, 45 | "list_tools": assistant.list_tools, 46 | }, 47 | ) 48 | 49 | # Run the example interaction 50 | await executor.a_initiate_chat(assistant, message=_get_test_prompt(), max_turns=5) 51 | 52 | 53 | def _get_system_message() -> str: 54 | """Get the system message for the assistant.""" 55 | return """You are an intelligent assistant with access to both Resources and Tools through the MCP (Model Context Protocol) interface: 56 | 57 | Resources (for accessing content): 58 | - Use read_resource for accessing file contents via URIs (e.g., "storage://local/config.txt") 59 | - Resources represent actual data and content you can read 60 | - Resource URIs follow the pattern: storage://local/{/path} 61 | 62 | Tools (for performing operations): 63 | - Discover available tools using list_tools 64 | - Each tool has: 65 | * A unique name (e.g., "write_file") 66 | * A description of its purpose 67 | * A schema defining its required arguments 68 | - To use a tool: 69 | 1. First call list_tools to discover available tools and their parameters 70 | 2. Then call_tool with: 71 | * name: The tool's name (e.g., "write_file") 72 | * args: An object matching the tool's parameter schema 73 | 74 | Best Practices: 75 | 1. Always discover tools first - don't assume which tools are available 76 | 2. Use the exact parameter structure defined in each tool's schema 77 | 3. Tools are server-specific - different servers may provide different capabilities 78 | 4. Use Resources for reading content, Tools for actions and modifications 79 | 80 | Example workflow: 81 | 1. List available tools to discover capabilities 82 | 2. Match tool parameters exactly to their schemas 83 | 3. Use tools and resources appropriately for your task 84 | """ 85 | 86 | 87 | def _get_test_prompt() -> str: 88 | """Get the test prompt for demonstrating tool usage.""" 89 | return """Please perform the following tasks: 90 | 91 | 1. Create three files named 'config1.txt', 'config2.txt', and 'config3.txt', each containing a different configuration number (1, 2, and 3 respectively) 92 | 2. Then read all three files simultaneously to verify their contents 93 | 3. Finally, create a summary.txt file containing the total number of config files created 94 | 95 | This will demonstrate both sequential operations (when needed) and parallel operations (when beneficial). 96 | """ 97 | 98 | 99 | if __name__ == "__main__": 100 | asyncio.run(example()) 101 | -------------------------------------------------------------------------------- /mcp_agent.py: -------------------------------------------------------------------------------- 1 | """ 2 | MCP-enabled AutoGen Assistant Agent. 3 | 4 | This module provides an extension of AutoGen's AssistantAgent that can interact with 5 | MCP (Model Context Protocol) servers, enabling dynamic tool discovery and resource access. 6 | """ 7 | 8 | from autogen import AssistantAgent 9 | from typing import Dict, Optional, List, Any, Union 10 | from mcp.client.session import ClientSession 11 | from mcp.client.stdio import stdio_client, StdioServerParameters 12 | 13 | 14 | class MCPAssistantAgent(AssistantAgent): 15 | """An AutoGen assistant agent with MCP capabilities. 16 | 17 | This agent extends the standard AutoGen AssistantAgent with the ability to: 18 | - Discover and use tools dynamically from MCP servers 19 | - Access resources through the MCP protocol 20 | - Handle both synchronous and asynchronous operations 21 | 22 | Attributes: 23 | server_params (StdioServerParameters): Configuration for the MCP server connection 24 | session (Optional[ClientSession]): Active MCP client session when connected 25 | """ 26 | 27 | def __init__( 28 | self, 29 | name: str, 30 | system_message: str, 31 | mcp_server_command: str, 32 | mcp_server_args: Optional[List[str]] = None, 33 | **kwargs, 34 | ): 35 | """Initialize the MCP-enabled assistant agent. 36 | 37 | Args: 38 | name: Name of the agent 39 | system_message: System message defining agent behavior 40 | mcp_server_command: Command to start the MCP server 41 | mcp_server_args: Optional arguments for the MCP server 42 | **kwargs: Additional arguments passed to AssistantAgent 43 | """ 44 | super().__init__(name=name, system_message=system_message, **kwargs) 45 | self.server_params = StdioServerParameters( 46 | command=mcp_server_command, args=mcp_server_args or [] 47 | ) 48 | 49 | @self.register_for_llm(description="Read content from a MCP resource") 50 | async def read_resource(uri: str) -> str: 51 | """Read content from an MCP resource. 52 | 53 | Args: 54 | uri: The URI of the resource (format: storage://local/path) 55 | 56 | Returns: 57 | str: The content of the resource 58 | 59 | Raises: 60 | Exception: If reading the resource fails 61 | """ 62 | try: 63 | async with stdio_client(self.server_params) as (read, write): 64 | async with ClientSession(read, write) as session: 65 | await session.initialize() 66 | return await session.read_resource(uri) 67 | except Exception as e: 68 | return f"Error reading resource: {str(e)}" 69 | 70 | @self.register_for_llm(description="Call a tool to perform an operation") 71 | async def call_tool(name: str, args: dict) -> Any: 72 | """Call an MCP tool with the specified arguments. 73 | 74 | Args: 75 | name: Name of the tool to call 76 | args: Arguments to pass to the tool 77 | 78 | Returns: 79 | Any: The result of the tool operation 80 | 81 | Raises: 82 | Exception: If the tool call fails 83 | """ 84 | try: 85 | async with stdio_client(self.server_params) as (read, write): 86 | async with ClientSession(read, write) as session: 87 | await session.initialize() 88 | result = await session.call_tool(name, args) 89 | if not result: 90 | return {"status": "success"} 91 | return result 92 | except Exception as e: 93 | return f"Error calling tool: {str(e)}" 94 | 95 | @self.register_for_llm(description="List available tools") 96 | async def list_tools() -> list[dict[str, Any]]: 97 | """Discover available tools from the MCP server. 98 | 99 | Returns: 100 | list[dict[str, Any]]: List of available tools and their schemas 101 | 102 | Raises: 103 | Exception: If tool discovery fails 104 | """ 105 | try: 106 | async with stdio_client(self.server_params) as (read, write): 107 | async with ClientSession(read, write) as session: 108 | await session.initialize() 109 | return await session.list_tools() 110 | except Exception as e: 111 | print(f"Error listing tools: {e}") 112 | raise 113 | 114 | self.read_resource = read_resource 115 | self.call_tool = call_tool 116 | self.list_tools = list_tools 117 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "mcp-ag2-example" 3 | version = "0.1.0" 4 | description = "Add your description here" 5 | readme = "README.md" 6 | requires-python = ">=3.11.10" 7 | dependencies = [ 8 | "mcp>=1.1.0", 9 | "pyautogen>=0.4.1", 10 | ] 11 | 12 | [build-system] 13 | requires = ["hatchling"] 14 | build-backend = "hatchling.build" 15 | 16 | [tool.hatch.build.targets.wheel] 17 | packages = ["server"] -------------------------------------------------------------------------------- /server/__init__.py: -------------------------------------------------------------------------------- 1 | # from .server import Server 2 | # from .local_file_server import LocalFileServer 3 | 4 | # __all__ = ["Server", "LocalFileServer"] -------------------------------------------------------------------------------- /server/local_file_server.py: -------------------------------------------------------------------------------- 1 | """ 2 | Local File System MCP Server. 3 | 4 | This module implements an MCP server that provides access to the local file system, 5 | enabling file operations through the Model Context Protocol (MCP). 6 | 7 | The server provides: 8 | 1. Resource-based file reading via storage://local/{/path} 9 | 2. Tool-based file writing via write_file tool 10 | """ 11 | 12 | import os 13 | import logging 14 | from pathlib import Path 15 | from typing import Optional, List 16 | from server.mcp_server import BaseMCPServer 17 | from mcp.types import ( 18 | Resource, 19 | Tool, 20 | ResourceTemplate, 21 | CallToolResult, 22 | TextContent 23 | ) 24 | from pydantic import AnyUrl, BaseModel, ValidationError 25 | from datetime import datetime 26 | 27 | # Configure logging 28 | logging.basicConfig( 29 | level=logging.INFO, 30 | format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' 31 | ) 32 | logger = logging.getLogger(__name__) 33 | 34 | class WriteFileParams(BaseModel): 35 | """Parameters for writing to a file""" 36 | 37 | path: str 38 | content: str 39 | 40 | model_config = { 41 | "json_schema_extra": { 42 | "examples": [{"path": "test.txt", "content": "Hello World"}] 43 | } 44 | } 45 | 46 | 47 | class WriteFileResponse(BaseModel): 48 | """Response from writing to a file""" 49 | 50 | path: str 51 | bytes_written: int 52 | modified_at: datetime 53 | 54 | 55 | class LocalFileServer(BaseMCPServer): 56 | """MCP server implementation for local file system operations. 57 | 58 | This server provides MCP-compatible access to the local file system, 59 | supporting both resource-based file reading and tool-based file writing. 60 | All operations are restricted to the specified base_path for security. 61 | """ 62 | 63 | def __init__(self, base_path: Optional[str] = None): 64 | """Initialize the local file server. 65 | 66 | Args: 67 | base_path: Optional base directory for file operations. 68 | Defaults to current working directory. 69 | """ 70 | try: 71 | self.base_path = Path(base_path or os.getcwd()).resolve() 72 | if not self.base_path.exists(): 73 | self.base_path.mkdir(parents=True) 74 | logger.info(f"Created base directory: {self.base_path}") 75 | 76 | super().__init__("local-file-server") 77 | logger.info(f"LocalFileServer initialized with base path: {self.base_path}") 78 | 79 | # Register handlers during initialization 80 | self._register_handlers() 81 | except Exception as e: 82 | logger.error(f"Failed to initialize LocalFileServer: {e}") 83 | raise 84 | 85 | def _register_handlers(self): 86 | """Register all MCP protocol handlers.""" 87 | try: 88 | # Resource handlers 89 | @self._server.list_resources() 90 | async def handle_list_resources(): 91 | """List available resources in the file system.""" 92 | return [ 93 | Resource( 94 | uri="storage://local/", 95 | name="Local Document Store", 96 | description="A local document store", 97 | mimeType="text/plain", 98 | ) 99 | ] 100 | 101 | @self._server.list_resource_templates() 102 | async def handle_list_resource_templates(): 103 | """List available resource templates.""" 104 | return [ 105 | ResourceTemplate( 106 | uriTemplate="storage://local/{/path}", 107 | name="Local Document Store", 108 | description="A local document store", 109 | mimeType="text/plain", 110 | ) 111 | ] 112 | 113 | @self._server.read_resource() 114 | async def handle_read_resource(uri: str) -> str: 115 | """Read a resource from the file system. 116 | 117 | Args: 118 | uri: URI of the resource to read (format: storage://local/path) 119 | 120 | Returns: 121 | str: Content of the resource 122 | 123 | Raises: 124 | FileNotFoundError: If the resource doesn't exist 125 | ValueError: If the URI is invalid 126 | """ 127 | try: 128 | uri_str = str(uri) 129 | if not uri_str.startswith("storage://local/"): 130 | raise ValueError(f"Invalid URI format: {uri}") 131 | 132 | path = uri_str.split("storage://local/")[1] 133 | full_path = (self.base_path / path).resolve() 134 | 135 | # Security check - ensure path is within base_path 136 | if not str(full_path).startswith(str(self.base_path)): 137 | raise ValueError(f"Access denied: Path {path} is outside base directory") 138 | 139 | if not full_path.exists(): 140 | raise FileNotFoundError(f"Resource not found: {path}") 141 | 142 | with open(full_path, "r") as f: 143 | content = f.read() 144 | logger.debug(f"Successfully read resource: {path}") 145 | return content 146 | except Exception as e: 147 | logger.error(f"Error reading resource {uri}: {e}") 148 | raise 149 | 150 | # Tool handlers 151 | @self._server.list_tools() 152 | async def handle_list_tools(): 153 | """List available tools for file operations.""" 154 | return [ 155 | Tool( 156 | name="write_file", 157 | description=( 158 | "Write content to a file. Path is relative to server's " 159 | "base directory." 160 | ), 161 | inputSchema=WriteFileParams.model_json_schema() 162 | ) 163 | ] 164 | 165 | @self._server.call_tool() 166 | async def handle_call_tool( 167 | name: str, 168 | arguments: dict | None 169 | ) -> list[TextContent]: 170 | """Handle tool invocation with proper response formatting. 171 | 172 | Args: 173 | name: Name of the tool to call 174 | arguments: Arguments for the tool 175 | 176 | Returns: 177 | list[TextContent]: Tool execution results 178 | """ 179 | try: 180 | tool_handlers = { 181 | "write_file": self._write_file, 182 | } 183 | 184 | if name not in tool_handlers: 185 | raise ValueError(f"Unknown tool: {name}") 186 | 187 | handler = tool_handlers[name] 188 | model_map = { 189 | "write_file": WriteFileParams, 190 | } 191 | 192 | input_model = model_map[name] 193 | try: 194 | validated_args = input_model.model_validate(arguments or {}) 195 | except ValidationError as e: 196 | logger.error(f"Validation error for tool {name}: {e}") 197 | return [self.format_error(e)] 198 | 199 | try: 200 | result = await handler(validated_args) 201 | logger.info(f"Successfully executed tool {name}") 202 | return [self.format_response(result)] 203 | except Exception as e: 204 | logger.error(f"Error executing tool {name}: {e}") 205 | return [self.format_error(e)] 206 | 207 | except Exception as e: 208 | logger.error(f"Unexpected error in tool handler: {e}") 209 | return [self.format_error(e)] 210 | 211 | logger.info("Successfully registered all MCP handlers") 212 | except Exception as e: 213 | logger.error(f"Failed to register handlers: {e}") 214 | raise 215 | 216 | async def _write_file(self, params: WriteFileParams) -> WriteFileResponse: 217 | """Write content to a file""" 218 | full_path = self.base_path / params.path 219 | try: 220 | with open(full_path, "w") as f: 221 | f.write(params.content) 222 | stats = full_path.stat() 223 | return WriteFileResponse( 224 | path=params.path, 225 | bytes_written=stats.st_size, 226 | modified_at=datetime.fromtimestamp(stats.st_mtime), 227 | ) 228 | except Exception as e: 229 | raise RuntimeError(f"Failed to write file: {e}") 230 | 231 | 232 | async def run(): 233 | """Run the MCP server using stdio transport. 234 | 235 | This function initializes and runs the server, handling command-line arguments 236 | and setting up the stdio communication channel. 237 | """ 238 | import argparse 239 | from mcp.server.stdio import stdio_server 240 | 241 | parser = argparse.ArgumentParser(description="Local File System MCP Server") 242 | parser.add_argument( 243 | "--path", 244 | type=str, 245 | default="./data", 246 | help="Base directory for file operations (default: ./data)" 247 | ) 248 | parser.add_argument( 249 | "--log-level", 250 | type=str, 251 | choices=["DEBUG", "INFO", "WARNING", "ERROR"], 252 | default="INFO", 253 | help="Set the logging level (default: INFO)" 254 | ) 255 | args = parser.parse_args() 256 | 257 | # Update log level if specified 258 | logger.setLevel(getattr(logging, args.log_level)) 259 | 260 | try: 261 | logger.info(f"Starting LocalFileServer with path: {args.path}") 262 | local_server = LocalFileServer(args.path) 263 | 264 | async with stdio_server() as streams: 265 | logger.info("Server started, waiting for connections...") 266 | await local_server._server.run( 267 | streams[0], 268 | streams[1], 269 | local_server._server.create_initialization_options() 270 | ) 271 | except KeyboardInterrupt: 272 | logger.info("Server shutdown requested") 273 | except Exception as e: 274 | logger.error(f"Server error: {e}") 275 | raise 276 | 277 | 278 | if __name__ == "__main__": 279 | import asyncio 280 | try: 281 | asyncio.run(run()) 282 | except Exception as e: 283 | logger.critical(f"Fatal server error: {e}") 284 | raise 285 | -------------------------------------------------------------------------------- /server/mcp_server.py: -------------------------------------------------------------------------------- 1 | """ 2 | Base MCP Server Implementation. 3 | 4 | This module provides a base class for implementing MCP servers with common functionality 5 | for handling resources, tools, and responses. 6 | """ 7 | 8 | from typing import Optional, Dict, Any 9 | from mcp.server import Server 10 | from mcp.types import Resource, Tool, TextContent 11 | from pydantic import BaseModel 12 | 13 | 14 | class BaseMCPServer: 15 | """Base class for MCP servers with common functionality. 16 | 17 | This class provides the foundation for building MCP servers with standardized 18 | handling of resources, tools, and response formatting. 19 | 20 | Attributes: 21 | _name: Name of the MCP server 22 | _server: Underlying MCP server instance 23 | """ 24 | 25 | def __init__(self, name: str): 26 | self._name = name 27 | self._server = Server(self._name) 28 | 29 | def format_response(self, response: BaseModel) -> TextContent: 30 | """Format a Pydantic model response as TextContent""" 31 | return TextContent( 32 | type="text", 33 | 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