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MCP Agent
Build effective agents using Model Context Protocol and simple workflow patterns
Install
uvx mcp-agent initclaude mcp add mcp-agent -- uvx mcp-agent init{
"mcpServers": {
"mcp-agent": {
"command": "uvx",
"args": [
"mcp-agent",
"init"
]
}
}
}Install command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →Build effective agents with Model Context Protocol using simple, composable patterns.
Examples | Building Effective Agents | MCP
Overview
mcp-agent is a simple, composable framework to build effective agents using Model Context Protocol.
[!Note] mcp-agent's vision is that MCP is all you need to build agents, and that simple patterns are more robust than complex architectures for shipping high-quality agents.
mcp-agent gives you the following:
- Full MCP support: It fully implements MCP, and handles the pesky business of managing the lifecycle of MCP server connections so you don't have to.
- Effective agent patterns: It implements every pattern described in Anthropic's Building Effective Agents in a composable way, allowing you to chain these patterns together.
- Durable agents: It works for simple agents and scales to sophisticated workflows built on Temporal so you can pause, resume, and recover without any API changes to your agent.
Altogether, this is the simplest and easiest way to build robust agent applications.
We welcome all kinds of contributions, feedback and your help in improving this project.
Minimal example
import asyncio
from mcp_agent.app import MCPApp
from mcp_agent.agents.agent import Agent
from mcp_agent.workflows.llm.augmented_llm_openai import OpenAIAugmentedLLM
app = MCPApp(name="hello_world")
async def main():
async with app.run():
agent = Agent(
name="finder",
instruction="Use filesystem and fetch to answer questions.",
server_names=["filesystem", "fetch"],
)
async with agent:
llm = await agent.attach_llm(OpenAIAugmentedLLM)
answer = await llm.generate_str("Summarize README.md in two sentences.")
print(answer)
if __name__ == "__main__":
asyncio.run(main())
# Add your LLM API key to `mcp_agent.secrets.yaml` or set it in env.
# The [Getting Started guide](https://docs.mcp-agent.com/get-started/overview) walks through configuration and secrets in detail.
At a glance
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