forked from mcp-use/mcp-use
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathfilesystem_use.py
More file actions
58 lines (45 loc) · 1.48 KB
/
filesystem_use.py
File metadata and controls
58 lines (45 loc) · 1.48 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
"""
Basic usage example for mcp_use.
This example demonstrates how to use the mcp_use library with MCPClient
to connect any LLM to MCP tools through a unified interface.
Special Thanks to https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem
for the server.
"""
import asyncio
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from mcp_use import MCPAgent, MCPClient
config = {
"mcpServers": {
"filesystem": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"THE_PATH_TO_YOUR_DIRECTORY",
],
}
}
}
async def main():
"""Run the example using a configuration file."""
# Load environment variables
load_dotenv()
# Create MCPClient from config file
client = MCPClient.from_dict(config)
# Create LLM
llm = ChatOpenAI(model="gpt-5")
# llm = init_chat_model(model="llama-3.1-8b-instant", model_provider="groq")
# llm = ChatAnthropic(model="claude-3-")
# llm = ChatGroq(model="llama3-8b-8192")
# Create agent with the client
agent = MCPAgent(llm=llm, client=client, max_steps=30, pretty_print=True)
# Run the query
result = await agent.run(
"Hello can you give me a list of files and directories in the current directory",
max_steps=30,
)
print(f"\nResult: {result}")
if __name__ == "__main__":
# Run the appropriate example
asyncio.run(main())