Model Context Protocol (MCP)

uv pip install "agno[mcp]" anthropic openai
export ANTHROPIC_API_KEY="your_anthropic_api_key"
export OPENAI_API_KEY="your_openai_api_key"

import asyncio

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.mcp import MCPTools

# Create the Agent
agno_agent = Agent(
    name="Agno Agent",
    model=Claude(id="claude-sonnet-4-5"),
    # Add the Agno MCP server to the Agent
    tools=[MCPTools(transport="streamable-http", url="https://docs.agno.com/mcp")],
)

if __name__ == "__main__":
    asyncio.run(agno_agent.aprint_response("How does Agno support MCP?"))

import asyncio
from pathlib import Path
from shlex import quote
from textwrap import dedent

from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.tools.mcp import MCPTools


async def run_agent(message: str) -> None:
    """Run the filesystem agent with the given message."""

    file_path = "<path to the directory you want to explore>"

    # Initialize and connect to the MCP server to access the filesystem
    mcp_tools = MCPTools(
        command=f"npx -y @modelcontextprotocol/server-filesystem {quote(str(Path(file_path).resolve()))}",
        include_tools=[
            "list_allowed_directories",
            "list_directory",
            "read_file",
        ],
    )
    await mcp_tools.connect()

    try:
        agent = Agent(
            model=OpenAIResponses(id="gpt-5.2"),
            tools=[mcp_tools],
            instructions=dedent("""\
                You are a filesystem assistant. Help users explore files and directories.

                - Navigate the filesystem to answer questions
                - Use the list_allowed_directories tool to find directories that you can access
                - Provide clear context about files you examine
                - Use headings to organize your responses
                - Be concise and focus on relevant information\
            """),
            markdown=True,
        )

        # Run the agent
        await agent.aprint_response(message, stream=True)
    finally:
        # Always close the connection when done
        await mcp_tools.close()


# Example usage
if __name__ == "__main__":
    # Basic example - exploring project license
    asyncio.run(run_agent("What is the license for this project?"))

mcp_tools = MCPTools(command="uvx mcp-server-git")
await mcp_tools.connect()

await mcp_tools.close()

mcp_tools = MCPTools(command="uvx mcp-server-git")
agent = Agent(model=OpenAIResponses(id="gpt-5.2"), tools=[mcp_tools])
await agent.aprint_response("What is the license for this project?", stream=True)  # The connection is established and closed on each run.

async with MCPTools(command="uvx mcp-server-git") as mcp_tools:
    agent = Agent(model=OpenAIResponses(id="gpt-5.2"), tools=[mcp_tools])
    await agent.aprint_response("What is the license for this project?", stream=True)

mcp_tools = MCPTools(command="uvx mcp-server-git", refresh_connection=True)
await mcp_tools.connect()

agent = Agent(model=OpenAIResponses(id="gpt-5.2"), tools=[mcp_tools])
await agent.aprint_response("What is the license for this project?", stream=True)  # The connection is checked and tools are refreshed on each run.

await mcp_tools.close()

mcp_tools = MCPTools(command="uvx mcp-server-git")
await mcp_tools.connect()

try:
    # Your agent code here
    pass
finally:
    await mcp_tools.close()

instructions = """
You are a filesystem assistant. Help users explore files and directories.
- Navigate the filesystem to answer questions
- Use the list_allowed_directories tool to find accessible directories
- Provide clear context about files you examine
- Be concise and focus on relevant information
"""