filesystem.py
"""MCP Filesystem Agent - Your Personal File Explorer!
This example shows how to create a filesystem agent that uses MCP to explore,
analyze, and provide insights about files and directories. The agent leverages the Model
Context Protocol (MCP) to interact with the filesystem, allowing it to answer questions
about file contents, directory structures, and more.
Example prompts to try:
- "What files are in the current directory?"
- "Show me the content of README.md"
- "What is the license for this project?"
- "Find all Python files in the project"
- "Summarize the main functionality of the codebase"
Run: `uv pip install agno mcp openai` to install the dependencies
"""
import asyncio
from pathlib import Path
from textwrap import dedent
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.mcp import MCPTools
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
async def run_agent(message: str) -> None:
"""Run the filesystem agent with the given message."""
# Initialize the MCP server
file_path = str(Path(__file__).parent.parent.parent.parent)
# Create a client session to connect to the MCP server
async with MCPTools(
f"npx -y @modelcontextprotocol/server-filesystem {file_path}"
) as mcp_tools:
agent = Agent(
model=OpenAIChat(id="gpt-4o"),
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)
# Example usage
# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
# Basic example - exploring project license
asyncio.run(run_agent("What is the license for this project?"))
# File content example
asyncio.run(
run_agent("Show me the content of README.md and explain what this project does")
)
# More example prompts to explore:
"""
File exploration queries:
1. "What are the main Python packages used in this project?"
2. "Show me all configuration files and explain their purpose"
3. "Find all test files and summarize what they're testing"
4. "What's the project's entry point and how does it work?"
5. "Analyze the project's dependency structure"
Code analysis queries:
1. "Explain the architecture of this codebase"
2. "What design patterns are used in this project?"
3. "Find potential security issues in the codebase"
4. "How is error handling implemented across the project?"
5. "Analyze the API endpoints in this project"
Documentation queries:
1. "Generate a summary of the project documentation"
2. "What features are documented but not implemented?"
3. "Are there any TODOs or FIXMEs in the codebase?"
4. "Create a high-level overview of the project's functionality"
5. "What's missing from the documentation?"
"""
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U "agno[mcp]" openai
3
Prepare Node.js
The MCP server runs with
npx. Install Node.js, then verify the commands:node --version
npx --version
4
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
5
Run the example
Clone Agno and run the example from the repository root:
git clone https://github.com/agno-agi/agno.git
cd agno
python cookbook/91_tools/mcp/filesystem.py