main.py•4.07 kB
import asyncio
import os
import time
from mcp_agent.app import MCPApp
from mcp_agent.config import (
Settings,
LoggerSettings,
MCPSettings,
MCPServerSettings,
OpenAISettings,
AnthropicSettings,
)
from mcp_agent.agents.agent import Agent
from mcp_agent.workflows.llm.augmented_llm import RequestParams
from mcp_agent.workflows.llm.llm_selector import ModelPreferences
from mcp_agent.workflows.llm.augmented_llm_anthropic import AnthropicAugmentedLLM
from mcp_agent.workflows.llm.augmented_llm_openai import OpenAIAugmentedLLM
settings = Settings(
execution_engine="asyncio",
logger=LoggerSettings(type="file", level="debug"),
mcp=MCPSettings(
servers={
"fetch": MCPServerSettings(
command="uvx",
args=["mcp-server-fetch"],
),
"filesystem": MCPServerSettings(
command="npx",
args=["-y", "@modelcontextprotocol/server-filesystem"],
),
}
),
openai=OpenAISettings(
api_key="sk-my-openai-api-key",
default_model="gpt-4o-mini",
),
anthropic=AnthropicSettings(
api_key="sk-my-anthropic-api-key",
),
)
# Settings can either be specified programmatically,
# or loaded from mcp_agent.config.yaml/mcp_agent.secrets.yaml
app = MCPApp(name="mcp_basic_agent") # settings=settings)
async def example_usage():
async with app.run() as agent_app:
logger = agent_app.logger
context = agent_app.context
logger.info("Current config:", data=context.config.model_dump())
# Add the current directory to the filesystem server's args
context.config.mcp.servers["filesystem"].args.extend([os.getcwd()])
finder_agent = Agent(
name="finder",
instruction="""You are an agent with access to the filesystem,
as well as the ability to fetch URLs. Your job is to identify
the closest match to a user's request, make the appropriate tool calls,
and return the URI and CONTENTS of the closest match.""",
server_names=["fetch", "filesystem"],
)
async with finder_agent:
logger.info("finder: Connected to server, calling list_tools...")
result = await finder_agent.list_tools()
logger.info("Tools available:", data=result.model_dump())
llm = await finder_agent.attach_llm(OpenAIAugmentedLLM)
result = await llm.generate_str(
message="Print the contents of mcp_agent.config.yaml verbatim",
)
logger.info(f"mcp_agent.config.yaml contents: {result}")
# Let's switch the same agent to a different LLM
llm = await finder_agent.attach_llm(AnthropicAugmentedLLM)
result = await llm.generate_str(
message="Print the first 2 paragraphs of https://modelcontextprotocol.io/introduction",
)
logger.info(f"First 2 paragraphs of Model Context Protocol docs: {result}")
# Multi-turn conversations
result = await llm.generate_str(
message="Summarize those paragraphs in a 128 character tweet",
# You can configure advanced options by setting the request_params object
request_params=RequestParams(
# See https://modelcontextprotocol.io/docs/concepts/sampling#model-preferences for more details
modelPreferences=ModelPreferences(
costPriority=0.1, speedPriority=0.2, intelligencePriority=0.7
),
# You can also set the model directly using the 'model' field
# Generally request_params type aligns with the Sampling API type in MCP
),
)
logger.info(f"Paragraph as a tweet: {result}")
if __name__ == "__main__":
start = time.time()
asyncio.run(example_usage())
end = time.time()
t = end - start
print(f"Total run time: {t:.2f}s")