add-mcp-integration
Wire a Model Context Protocol (MCP) server into AG2 agents using create_toolkit. Use when the user wants to connect external tools via MCP.
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- リポジトリ
- ag2ai/cursor-plugin
- ソースの最終更新活動
- 2026年3月12日 03:00
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ソースの指示 · 読み取り専用プレビュー- name
- add-mcp-integration
- description
- Wire a Model Context Protocol (MCP) server into AG2 agents using create_toolkit. Use when the user wants to connect external tools via MCP.
# Add MCP Integration to AG2
You are an expert at integrating MCP servers with AG2 agents.
## 1. Understand the MCP Server
- What MCP server does the user want to connect?
- Is it a stdio-based server (runs as subprocess) or SSE-based (HTTP endpoint)?
- What tools does the server provide?
## 2. Generate the Integration
### Stdio Server (most common)
```python
import asyncio
from autogen import ConversableAgent, LLMConfig
from autogen.mcp import create_toolkit
from autogen.mcp.mcp_client import StdioConfig
async def main():
llm_config = LLMConfig(
{"model": "gpt-4o-mini", "api_key": os.environ["OPENAI_API_KEY"]}
)
agent = ConversableAgent(
name="assistant",
llm_config=llm_config,
human_input_mode="NEVER",
)
# Configure the MCP server
stdio_config = StdioConfig(
command="npx",
args=["-y", "@modelcontextprotocol/server-filesystem", "/path/to/dir"],
)
# Create toolkit from MCP server and register with agent
async with stdio_config.connect() as session:
toolkit = await create_toolkit(session)
toolkit.register_for_llm(agent)
toolkit.register_for_execution(agent)
# Now use the agent — it has all MCP tools available
user_proxy = ConversableAgent(
name="user",
llm_config=False,
human_input_mode="NEVER",
)
result = await user_proxy.a_run(
agent,
message="List files in the directory",
)
await result.process()
asyncio.run(main())
```
### SSE Server
```python
from autogen.mcp.mcp_client import SseConfig
sse_config = SseConfig(
url="http://localhost:8080/sse",
headers={"Authorization": f"Bearer {os.environ['MCP_TOKEN']}"},
timeout=5,
sse_read_timeout=300,
)
async with sse_config.connect() as session:
toolkit = await create_toolkit(session)
toolkit.register_for_llm(agent)
toolkit.register_for_execution(agent)
```
## 3. StdioConfig Parameters
```python
StdioConfig(
command="npx", # Command to run
args=["-y", "server-package"], # Arguments
environment={"KEY": "value"}, # Optional env vars
working_dir="/path/to/dir", # Optional working directory
encoding="utf-8", # Default
)
```
## 4. create_toolkit Options
```python
toolkit = await create_toolkit(
session,
use_mcp_tools=True, # Import tools from MCP server
use_mcp_resources=True, # Import resources from MCP server
resource_download_folder="./mcp_resources", # Where to save resources
)
```
## 5. Requirements
- Install MCP support: `pip install ag2[mcp]`
- MCP integration is async — use `async/await` and `a_run`
- The MCP server must be running during the agent conversation
- Use context manager (`async with`) to properly manage server lifecycle
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