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Build effective agents with Model Context Protocol using simple, composable patterns.
Examples | Building Effective Agents | MCP
mcp-agent is a simple, composable framework to build agents using Model Context Protocol.
Inspiration: Anthropic announced 2 foundational updates for AI application developers:
mcp-agent puts these two foundational pieces into an AI application framework:
Altogether, this is the simplest and easiest way to build robust agent applications. Much like MCP, this project is in early development. We welcome all kinds of contributions, feedback and your help in growing this to become a new standard.
We recommend using uv to manage your Python projects:
uv add "mcp-agent"
Alternatively:
pip install mcp-agent
[!TIP] The
examplesdirectory has several example applications to get started with. To run an example, clone this repo, then:cd examples/basic/mcp_basic_agent # Or any other example cp mcp_agent.secrets.yaml.example mcp_agent.secrets.yaml # Update API keys uv run main.py
Here is a basic "finder" agent that uses the fetch and filesystem servers to look up a file, read a blog and write a tweet. Example link:
import asyncio
import os
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_agent")
async def example_usage():
async with app.run() as mcp_agent_app:
logger = mcp_agent_app.logger
# This agent can read the filesystem or fetch URLs
finder_agent = Agent(
name="finder",
instruction="""You can read local files or fetch URLs.
Return the requested information when asked.""",
server_names=["fetch", "filesystem"], # MCP servers this Agent can use
)
async with finder_agent:
# Automatically initializes the MCP servers and adds their tools for LLM use
tools = await finder_agent.list_tools()
logger.info(f"Tools available:", data=tools)
# Attach an OpenAI LLM to the agent (defaults to GPT-4o)
llm = await finder_agent.attach_llm(OpenAIAugmentedLLM)
# This will perform a file lookup and read using the filesystem server
result = await llm.generate_str(
message="Show me what's in README.md verbatim"
)
logger.info(f"README.md contents: {result}")
# Uses the fetch server to fetch the content from URL
result = await llm.generate_str(
message="Print the first two paragraphs from https://www.anthropic.com/research/building-effective-agents"
)
logger.info(f"Blog intro: {result}")
# Multi-turn interactions by default
result = await llm.generate_str("Summarize that in a 128-char tweet")
logger.info(f"Tweet: {result}")
if __name__ == "__main__":
asyncio.run(example_usage())
execution_engine: asyncio
logger:
transports: [console] # You can use [file, console] for both
level: debug
path: "logs/mcp-agent.jsonl" # Used for file transport
# For dynamic log filenames:
# path_settings:
# path_pattern: "logs/mcp-agent-{unique_id}.jsonl"
# unique_id: "timestamp" # Or "session_id"
# timestamp_format: "%Y%m%d_%H%M%S"
mcp:
servers:
fetch:
command: "uvx"
args: ["mcp-server-fetch"]
filesystem:
command: "npx"
args:
[
"-y",
"@modelcontextprotocol/server-filesystem",
"<add_your_directories>",
]
openai:
# Secrets (API keys, etc.) are stored in an mcp_agent.secrets.yaml file which can be gitignored
default_model: gpt-4o
mcp-agent?There are too many AI frameworks out there already. But mcp-agent is the only one that is purpose-built for a shared protocol - MCP. It is also the most lightweight, and is closer to an agent pattern library than a framework.
As more services become MCP-aware, you can use mcp-agent to build robust and controllable AI agents that can leverage those services out-of-the-box.
Before we go into the core concepts of mcp-agent, let's show what you can build with it.
In short, you can build any kind of AI application with mcp-agent: multi-agent collaborative workflows, human-in-the-loop workflows, RAG pipelines and more.
You can integrate mcp-agent apps into MCP clients like Claude Desktop.
This app wraps an mcp-agent application inside an MCP server, and exposes that server to Claude Desktop. The app exposes agents and workflows that Claude Desktop can invoke to service of the user's request.
https://github.com/user-attachments/assets/7807cffd-dba7-4f0c-9c70-9482fd7e0699
This demo shows a multi-agent evaluation task where each agent evaluates aspects of an input poem, and then an aggregator summarizes their findings into a final response.
Details: Starting from a user's request over text, the application:
Link to code: examples/basic/mcp_agent_server
[!NOTE] Huge thanks to Jerron Lim (@StreetLamb) for developing and contributing this example!
You can deploy mcp-agent apps using Streamlit.
This app is able to perform read and write actions on gmail using text prompts -- i.e. read, delete, send emails, mark as read/unread, etc. It uses an MCP server for Gmail.
https://github.com/user-attachments/assets/54899cac-de24-4102-bd7e-4b2022c956e3
Link to code: gmail-mcp-server
[!NOTE] Huge thanks to Jason Summer (@jasonsum) for developing and contributing this example!
This app uses a Qdrant vector database (via an MCP server) to do Q&A over a corpus of text.
https://github.com/user-attachments/assets/f4dcd227-cae9-4a59-aa9e-0eceeb4acaf4
Link to code: examples/usecases/streamlit_mcp_rag_agent
[!NOTE] Huge thanks to Jerron Lim (@StreetLamb) for developing and contributing this example!
Marimo is a reactive Python notebook that replaces Jupyter and Streamlit. Here's the "file finder" agent from Quickstart implemented in Marimo:
Link to code: examples/usecases/marimo_mcp_basic_agent
[!NOTE] Huge thanks to Akshay Agrawal (@akshayka) for developing and contributing this example!
You can write mcp-agent apps as Python scripts or Jupyter notebooks.
This example demonstrates a multi-agent setup for handling different customer service requests in an airline context using the Swarm workflow pattern. The agents can triage requests, handle flight modifications, cancellations, and lost baggage cases.
https://github.com/user-attachments/assets/b314d75d-7945-4de6-965b-7f21eb14a8bd
Link to code: examples/workflows/workflow_swarm
The following are the building blocks of the mcp-agent framework:
gen_client and MCPConnectionManager to easily connect to MCP servers.AugmentedLLM itself, allowing you to compose and chain them together.Everything in the framework is a derivative of these core capabilities.
mcp-agent provides implementations for every pattern in Anthropic’s Building Effective Agents, as well as the OpenAI Swarm pattern.
Each pattern is model-agnostic, and exposed as an AugmentedLLM, making everything very composable.
AugmentedLLM is an LLM that has access to MCP servers and functions via Agents.
LLM providers implement the AugmentedLLM interface to expose 3 functions:
generate: Generate message(s) given a prompt, possibly over multiple iterations and making tool calls as needed.generate_str: Calls generate and returns result as a string output.generate_structured: Uses Instructor to return the generated result as a Pydantic model.Additionally, AugmentedLLM has memory, to keep track of long or short-term history.
from mcp_agent.agents.agent import Agent
from mcp_agent.workflows.llm.augmented_llm_anthropic import AnthropicAugmentedLLM
finder_agent = Agent(
name="finder",
instruction="You are an agent with filesystem + fetch access. Return the requested file or URL contents.",
server_names=["fetch", "filesystem"],
)
async with finder_agent:
llm = await finder_agent.attach_llm(AnthropicAugmentedLLM)
result = await llm.generate_str(
message="Print the first 2 paragraphs of https://www.anthropic.com/research/building-effective-agents",
# Can override model, tokens and other defaults
)
logger.info(f"Result: {result}")
# Multi-turn conversation
result = await llm.generate_str(
message="Summarize those paragraphs in a 128 character tweet",
)
logger.info(f"Result: {result}")
Fan-out tasks to multiple sub-agents and fan-in the results. Each subtask is an AugmentedLLM, as is the overall Parallel workflow, meaning each subtask can optionally be a more complex workflow itself.
[!NOTE]
proofreader = Agent(name="proofreader", instruction="Review grammar...")
fact_checker = Agent(name="fact_checker", instruction="Check factual consistency...")
style_enforcer = Agent(name="style_enforcer", instruction="Enforce style guidelines...")
grader = Agent(name="grader", instruction="Combine feedback into a structured report.")
parallel = ParallelLLM(
fan_in_agent=grader,
fan_out_agents=[proofreader, fact_checker, style_enforcer],
llm_factory=OpenAIAugmentedLLM,
)
result = await parallel.generate_str("Student short story submission: ...", RequestParams(model="gpt4-o"))
Given an input, route to the top_k most relevant categories. A category can be an Agent, an MCP server or a regular function.
mcp-agent provides several router implementations, including:
EmbeddingRouter: uses embedding models for classificationLLMRouter: uses LLMs for classification[!NOTE]
def print_hello_world:
print("Hello, world!")
finder_agent = Agent(name="finder", server_names=["fetch", "filesystem"])
writer_agent = Agent(name="writer", server_names=["filesystem"])
llm = OpenAIAugmentedLLM()
router = LLMRouter(
llm=llm,
agents=[finder_agent, writer_agent],
functions=[print_hello_world],
)
results = await router.route( # Also available: route_to_agent, route_to_server
request="Find and print the contents of README.md verbatim",
top_k=1
)
chosen_agent = results[0].result
async with chosen_agent:
...
A close sibling of Router, the Intent Classifier pattern identifies the top_k Intents that most closely match a given input.
Just like a Router, mcp-agent provides both an embedding and LLM-based intent classifier.
One LLM (the “optimizer”) refines a response, another (the “evaluator”) critiques it until a response exceeds a quality criteria.
[!NOTE]
optimizer = Agent(name="cover_letter_writer", server_names=["fetch"], instruction="Generate a cover letter ...")
evaluator = Agent(name="critiquer", instruction="Evaluate clarity, specificity, relevance...")
llm = EvaluatorOptimizerLLM(
optimizer=optimizer,
evaluator=evaluator,
llm_factory=OpenAIAugmentedLLM,
min_rating=QualityRating.EXCELLENT, # Keep iterating until the minimum quality bar is reached
)
result = await eo_llm.generate_str("Write a job cover letter for an AI framework developer role at LastMile AI.")
print("Final refined cover letter:", result)
A higher-level LLM generates a plan, then assigns them to sub-agents, and synthesizes the results. The Orchestrator workflow automatically parallelizes steps that can be done in parallel, and blocks on dependencies.
[!NOTE]
finder_agent = Agent(name="finder", server_names=["fetch", "filesystem"])
writer_agent = Agent(name="writer", server_names=["filesystem"])
proofreader = Agent(name="proofreader", ...)
fact_checker = Agent(name="fact_checker", ...)
style_enforcer = Agent(name="style_enforcer", instructions="Use APA style guide from ...", server_names=["fetch"])
orchestrator = Orchestrator(
llm_factory=AnthropicAugmentedLLM,
available_agents=[finder_agent, writer_agent, proofreader, fact_checker, style_enforcer],
)
task = "Load short_story.md, evaluate it, produce a graded_report.md with multiple feedback aspects."
result = await orchestrator.generate_str(task, RequestParams(model="gpt-4o"))
print(result)
OpenAI has an experimental multi-agent pattern called Swarm, which we provide a model-agnostic reference implementation for in mcp-agent.
The mcp-agent Swarm pattern works seamlessly with MCP servers, and is exposed as an AugmentedLLM, allowing for composability with other patterns above.
[!NOTE]
triage_agent = SwarmAgent(...)
flight_mod_agent = SwarmAgent(...)
lost_baggage_agent = SwarmAgent(...)
# The triage agent decides whether to route to flight_mod_agent or lost_baggage_agent
swarm = AnthropicSwarm(agent=triage_agent, context_variables={...})
test_input = "My bag was not delivered!"
result = await swarm.generate_str(test_input)
print("Result:", result)
An example of composability is using an Evaluator-Optimizer workflow as the planner LLM inside the Orchestrator workflow. Generating a high-quality plan to execute is important for robust behavior, and an evaluator-optimizer can help ensure that.
Doing so is seamless in mcp-agent, because each workflow is implemented as an AugmentedLLM.
optimizer = Agent(name="plan_optimizer", server_names=[...], instruction="Generate a plan given an objective ...")
evaluator = Agent(name="plan_evaluator", instruction="Evaluate logic, ordering and precision of plan......")
planner_llm = EvaluatorOptimizerLLM(
optimizer=optimizer,
evaluator=evaluator,
llm_factory=OpenAIAugmentedLLM,
min_rating=QualityRating.EXCELLENT,
)
orchestrator = Orchestrator(
llm_factory=AnthropicAugmentedLLM,
available_agents=[finder_agent, writer_agent, proofreader, fact_checker, style_enforcer],
planner=planner_llm # It's that simple
)
...
Signaling: The framework can pause/resume tasks. The agent or LLM might “signal” that it needs user input, so the workflow awaits. A developer may signal during a workflow to seek approval or review before continuing with a workflow.
Human Input: If an Agent has a human_input_callback, the LLM can call a __human_input__ tool to request user input mid-workflow.
The Swarm example shows this in action.
from mcp_agent.human_input.handler import console_input_callback
lost_baggage = SwarmAgent(
name="Lost baggage traversal",
instruction=lambda context_variables: f"""
{
FLY_AIR_AGENT_PROMPT.format(
customer_context=context_variables.get("customer_context", "None"),
flight_context=context_variables.get("flight_context", "None"),
)
}\n Lost baggage policy: policies/lost_baggage_policy.md""",
functions=[
escalate_to_agent,
initiate_baggage_search,
transfer_to_triage,
case_resolved,
],
server_names=["fetch", "filesystem"],
human_input_callback=console_input_callback, # Request input from the console
)
Create an mcp_agent.config.yaml and a gitignored mcp_agent.secrets.yaml to define MCP app configuration. This controls logging, execution, LLM provider APIs, and MCP server configuration.
mcp-agent makes it trivial to connect to MCP servers. Create an mcp_agent.config.yaml to define server configuration under the mcp section:
mcp:
servers:
fetch:
command: "uvx"
args: ["mcp-server-fetch"]
description: "Fetch content at URLs from the world wide web"
gen_clientManage the lifecycle of an MCP server within an async context manager:
from mcp_agent.mcp.gen_client import gen_client
async with gen_client("fetch") as fetch_client:
# Fetch server is initialized and ready to use
result = await fetch_client.list_tools()
# Fetch server is automatically disconnected/shutdown
The gen_client function makes it easy to spin up connections to MCP servers.
In many cases, you want an MCP server to stay online for persistent use (e.g. in a multi-step tool use workflow). For persistent connections, use:
connect and disconnectfrom mcp_agent.mcp.gen_client import connect, disconnect
fetch_client = None
try:
fetch_client = connect("fetch")
result = await fetch_client.list_tools()
finally:
disconnect("fetch")
MCPConnectionManager
For even more fine-grained control over server connections, you can use the MCPConnectionManager.from mcp_agent.context import get_current_context
from mcp_agent.mcp.mcp_connection_manager import MCPConnectionManager
context = get_current_context()
connection_manager = MCPConnectionManager(context.server_registry)
async with connection_manager:
fetch_client = await connection_manager.get_server("fetch") # Initializes fetch server
result = fetch_client.list_tool()
fetch_client2 = await connection_manager.get_server("fetch") # Reuses same server connection
# All servers managed by connection manager are automatically disconnected/shut down
MCPAggregator acts as a "server-of-servers".
It provides a single MCP server interface for interacting with multiple MCP servers.
This allows you to expose tools from multiple servers to LLM applications.
from mcp_agent.mcp.mcp_aggregator import MCPAggregator
aggregator = await MCPAggregator.create(server_names=["fetch", "filesystem"])
async with aggregator:
# combined list of tools exposed by 'fetch' and 'filesystem' servers
tools = await aggregator.list_tools()
# namespacing -- invokes the 'fetch' server to call the 'fetch' tool
fetch_result = await aggregator.call_tool(name="fetch-fetch", arguments={"url": "https://www.anthropic.com/research/building-effective-agents"})
# no namespacing -- first server in the aggregator exposing that tool wins
read_file_result = await aggregator.call_tool(name="read_file", arguments={})
We welcome any and all kinds of contributions. Please see the CONTRIBUTING guidelines to get started.
There have already been incredible community contributors who are driving this project forward:
mcp-agent and generally to the MCP ecosystem.We will be adding a detailed roadmap (ideally driven by your feedback). The current set of priorities include:
mcp-agent provides a streamlined approach to building AI agents using capabilities exposed by MCP (Model Context Protocol) servers.
MCP is quite low-level, and this framework handles the mechanics of connecting to servers, working with LLMs, handling external signals (like human input) and supporting persistent state via durable execution. That lets you, the developer, focus on the core business logic of your AI application.
Core benefits:
if statements. For cycles, use while loops.No, you can use mcp-agent anywhere, since it handles MCPClient creation for you. This allows you to leverage MCP servers outside of MCP hosts like Claude Desktop.
Here's all the ways you can set up your mcp-agent application:
You can expose mcp-agent applications as MCP servers themselves (see example), allowing MCP clients to interface with sophisticated AI workflows using the standard tools API of MCP servers. This is effectively a server-of-servers.
You can embed mcp-agent in an MCP client directly to manage the orchestration across multiple MCP servers.
You can use mcp-agent applications in a standalone fashion (i.e. they aren't part of an MCP client). The examples are all standalone applications.
I debated naming this project silsila (سلسلہ), which means chain of events in Urdu. mcp-agent is more matter-of-fact, but there's still an easter egg in the project paying homage to silsila.
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