Integration of MCP with AutoGen AG2 by Jason TanIntegration of MCP with AutoGen AG2 by Jason Tan

Integration of MCP with AutoGen AG2

Jason Tan

AI Engineer

Backend Engineer

Anthropic

OpenAI

Python

Artificial Intelligence

MCP-AG2 Integration Example

This project demonstrates the integration of the Model Context Protocol (MCP) with AutoGen (AG2), showcasing a powerful pattern for building modular, tool-enabled AI agents.

Overview

The example implements three key components:

MCP Server: A process that exposes resources and tools following the MCP specification

MCPAssistantAgent: An AutoGen AssistantAgent extension that implements the MCP client interface

Example Script: Demonstrates an MCP-enabled agent using LLM capabilities with MCP resources/tools

Setup

Prerequisites

Install uv package manager:

# macOS
brew install uv

# Other platforms
curl -LsSf https://astral.sh/uv/install.sh | sh

Installation

# Clone the repository
git clone https://github.com/jtanningbed/mcp-ag2-example
cd mcp-ag2-example

# Install dependencies
uv sync

# Run the example
uv run example.py

Key Benefits

This integration pattern offers several advantages over traditional tool/function calling implementations:

1. Protocol-Level Interface Abstraction

The MCP client interface itself (read_resource, call_tool, etc.) is exposed to the LLM agent through AG2's tool registration

Rather than registering individual tools directly with AG2, we register only the core MCP interface methods

All specific tools (e.g., write_file) are proxied through the call_tool interface to the MCP server

This means tools defined on the MCP server don't need any format conversion for different LLMs - they remain in Anthropic schema format

2. Dynamic Tool Discovery and Model Agnosticism

Agents use list_tools to discover available server tools

While the MCP server defines tools using Anthropic's schema format, the LLM never sees these directly

The LLM only needs to understand how to use call_tool with a name and arguments

No need to convert tool schemas between different LLM formats since they're abstracted behind the MCP interface

3. Clean Separation of Concerns

MCP server handles:

Tool implementation details

Tool schema definitions (in Anthropic format)

Resource management

Agent only handles:

Understanding the core MCP interface methods

Using call_tool to proxy specific tool requests to the server

LLM integration layer only handles:

Registering MCP interface methods as tools (e.g., call_tool in OpenAI format)

Routing tool calls through the MCP client

AutoGen-Specific Benefits

When compared to traditional AutoGen tool implementations:

Simplified Tool Integration

No need to define tool schemas in multiple formats

Tools are defined once on the MCP server in Anthropic format

AG2 only needs the call_tool interface registered

Enhanced Modularity

MCP servers can be used by any MCP-compatible client

Tools and resources are completely decoupled from agent implementation

New tools can be added to the server without any agent changes

Architecture Overview

graph LR
    A[LLM] -->|Uses| B[AG2 Tool Interface]
    B -->|Registered| C[MCP Interface Methods]
    C -->|Proxy| D[MCP Client]
    D -->|Protocol| E[MCP Server]
    E -->|Implements| F[Tools & Resources]

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Posted Sep 26, 2026

Open-source pattern for wiring MCP tools into AutoGen (AG2) agents: register one interface, proxy every tool, no per-LLM schema conversion. ★42

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