Tiny Agents in Python: an MCP-powered agent in ~70 lines of code
Inspired by Tiny Agents in JS, we ported the idea to Python 🐍 and extended the
huggingface_hub client SDK to act as a MCP Client so it can pull tools from MCP servers and pass them to the LLM during inference.
MCP (Model Context Protocol) is an open protocol that standardizes how Large Language Models (LLMs) interact with external tools and APIs. Essentially, it removed the need to write custom integrations for each tool, making it simpler to plug new capabilities into your LLMs.
In this blog post, we'll show you how to get started with a tiny Agent in Python connected to MCP servers to unlock powerful tool capabilities. You'll see just how easy it is to spin up your own Agent and start building!
Spoiler : An Agent is essentially a while loop built right on top of an MCP Client!
How to Run the Demo
This section walks you through how to use existing Tiny Agents. We'll cover the setup and the commands to get an agent running.
First, you need to install the latest version of huggingface_hub with the mcp extra to get all the necessary components.
pip install "huggingface_hub[mcp]>=0.32.0"
Now, let's run an agent using the CLI!
The coolest part is that you can load agents directly from the Hugging Face Hub tiny-agents Dataset, or specify a path to your own local agent configuration!
> tiny-agents run --help
Usage: tiny-agents run [OPTIONS] [PATH] COMMAND [ARGS]...
Run the Agent in the CLI
╭─ Arguments ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ path [PATH] Path to a local folder containing an agent.json file or a built-in agent stored in the 'tiny-agents/tiny-agents' Hugging Face dataset │
│ (https://huggingface.co/datasets/tiny-agents/tiny-agents) │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --help Show this message and exit. │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
If you don't provide a path to a specific agent configuration, our Tiny Agent will connect by default to the following two MCP servers:
- the "canonical" file system server, which gets access to your Desktop,
- and the Playwright MCP server, which knows how to use a sandboxed Chromium browser for you.
The following example shows a web-browsing agent configured to use the Qwen/Qwen2.5-72B-Instruct model via Nebius inference provider, and it comes equipped with a playwright MCP server, which lets it use a web browser! The agent config is loaded specifying its path in the tiny-agents/tiny-agents Hugging Face dataset.
When you run the agent, you'll see it load, listing the tools it has discovered from its connected MCP servers. Then, it's ready for your prompts!
Prompt used in this demo:
do a Web Search for HF inference providers on Brave Search and open the first result and then give me the list of the inference providers supported on Hugging Face
You can also use Gradio Spaces as MCP servers! The following example uses Qwen/Qwen2.5-72B-Instruct model via Nebius inference provider, and connects to a FLUX.1 [schnell] image generation HF Space as an MCP server. The agent is loaded from its configuration in the tiny-agents/tiny-agents dataset on the Hugging Face Hub.
Prompt used in this demo:
Generate a 1024x1024 image of a tiny astronaut hatching from an egg on the surface of the moon.
Now that you've seen how to run existing Tiny Agents, the following sections will dive deeper into how they work and how to build your own.
Agent Configuration
Each agent's behavior (its default model, inference provider, which MCP servers to connect to, and its initial system prompt) is defined by an agent.json file. You can also provide a custom PROMPT.md in the same directory for a more detailed system prompt. Here is an example:
agent.json
The model and provider fields specify the LLM and inference provider used by the agent.
The servers array defines the MCP servers the agent will connect to.
In this example, a "stdio" MCP server is configured. This type of server runs as a local process. The Agent starts it using the specified command and args, and then communicates with it via stdin/stdout to discover and execute available tools.
{
"model": "Qwen/Qwen2.5-72B-Instruct",
"provider": "nebius",
"servers": [
{
"type": "stdio",
"config": {
"command": "npx",
"args": ["@playwright/mcp@latest"]
}
}
]
}
PROMPT.md
You are an agent - please keep going until the user’s query is completely resolved [...]
You can find more details about Hugging Face Inference Providers here.
LLMs Can Use Tools
Modern LLMs are built for function calling (or tool use), which enables users to easily build applications tailored to specific use cases and real-world tasks.
A function is defined by its schema, which informs the LLM what it does and what input arguments it expects. The LLM decides when to use a tool, the Agent then orchestrates running the tool and feeding the result back.
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current temperature for a given location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and country e.g. Paris, France"
}
},
"required": ["location"],
},
}
}
]
InferenceClient implements the same tool calling interface as the OpenAI Chat Completions API, which is the established standard for inference providers and the community.
Building our Python MCP Client
The MCPClient is the heart of our tool-use functionality. It's now part of huggingface_hub and uses the AsyncInferenceClient to communicate with LLMs.
The full
MCPClientcode is in here if you want to follow along using the actual code 🤓
Key responsibilities of the MCPClient:
- Manage async connections to one or more MCP servers.
- Discover tools from these servers.
- Format these tools for the LLM.
- Execute tool calls via the correct MCP server.
Here’s a glimpse of how it connects to an MCP server (the add_mcp_server method):
# Lines 111-219 of `MCPClient.add_mcp_server`
# https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_mcp/mcp_client.py#L111:L219
class MCPClient:
...
async def add_mcp_server(self, type: ServerType, **params: Any):
# 'type' can be "stdio", "sse", or "http"
# 'params' are specific to the server type, e.g.:
# for "stdio": {"command": "my_tool_server_cmd", "args": ["--port", "1234"]}
# for "http": {"url": "http://my.tool.server/mcp"}
# 1. Establish connection based on type (stdio, sse, http)
# (Uses mcp.client.stdio_client, sse_client, or streamablehttp_client)
read, write = await self.exit_stack.enter_async_context(...)
# 2. Create an MCP ClientSession
session = await self.exit_stack.enter_async_context(
ClientSession(read_stream=read, write_stream=write, ...)
)
await session.initialize()
# 3. List tools from the server
response = await session.list_tools()
for tool in response.tools:
# Store session for this tool
self.sessions[tool.name] = session
# Add tool to the list of available tools and Format for LLM
self.available_tools.append({
"type": "function",
"function": {
"name": tool.name,
"description": tool.description,
"parameters": tool.input_schema,
},
})
It supports stdio servers for local tools (like accessing your file system), and http servers for remote tools! It's also compatible with sse, which is the previous standard for remote tools.
Using the Tools: Streaming and Processing
The MCPClient's process_single_turn_with_tools method is where the LLM interaction happens. It sends the conversation history and available tools to the LLM via AsyncInferenceClient.chat.completions.create(..., stream=True).
1. Prepare tools and calling the LLM
First, the method determines all tools the LLM should be aware of for the current turn – this includes tools from MCP servers and any special "exit loop" tools for agent control; then, it makes a streaming call to the LLM:
# Lines 241-251 of `MCPClient.process_single_turn_with_tools`
# https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_mcp/mcp_client.py#L241:L251
# Prepare tools list based on options
tools = self.available_tools
if exit_loop_tools is not None:
tools = [*exit_loop_tools, *self.available_tools]
# Create the streaming request to the LLM
response = await self.client.chat.completions.create(
messages=messages,
tools=tools,
tool_choice="auto", # LLM decides if it needs a tool
stream=True,
)
As chunks arrive from the LLM, the method iterates through them. Each chunk is immediately yielded, then we reconstruct the complete text response and any tool calls.
# Lines 258-290 of `MCPClient.process_single_turn_with_tools`
# https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_mcp/mcp_client.py#L258:L290
# Read from stream
async for chunk in response:
# Yield each chunk to caller
yield chunk
# Aggregate LLM's text response and parts of tool calls
…
2. Executing tools
Once the stream is complete, if the LLM requested any tool calls (now fully reconstructed in final_tool_calls), the method processes each one:
# Lines 293-313 of `MCPClient.process_single_turn_with_tools`
# https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_mcp/mcp_client.py#L293:L313
for tool_call in final_tool_calls.values():
function_name = tool_call.function.name
function_args = json.loads(tool_call.function.arguments or "{}")
# Prepare a message to store the tool's result
tool_message = {"role": "tool", "tool_call_id": tool_call.id, "content": "", "name": function_name}
# a. Is this a special "exit loop" tool?
if exit_loop_tools and function_name in [t.function.name for t in exit_loop_tools]:
# If so, yield a message and terminate this turn's processing
messages.append(ChatCompletionInputMessage.parse_obj_as_instance(tool_message))
yield ChatCompletionInputMessage.parse_obj_as_instance(tool_message)
return # The Agent's main loop will handle this signal
# b. It's a regular tool: find the MCP session and execute it
session = self.sessions.get(function_name) # self.sessions maps tool names to MCP connections
if session is not None:
result = await session.call_tool(function_name, function_args)
tool_message["content"] = format_result(result) # format_result processes tool output
else:
tool_message["content"] = f"Error: No session found for tool: {function_name}"
tool_message["content"] = error_msg
# Add tool result to history and yield it
...
It first checks if the tool called exits the loop (exit_loop_tool). If not, it finds the correct MCP session responsible for that tool and calls session.call_tool(). The result (or error response) is then formatted, added to the conversation history, and yielded so the Agent is aware of the tool's output.
Our Tiny Python Agent: It's (Almost) Just a Loop!
With the MCPClient doing all the job for tool interactions, our Agent class becomes wonderfully simple. It inherits from MCPClient and adds the conversation management logic.
The Agent class is tiny and focuses on the conversational loop, the code can be found here.
1. Initializing the Agent
When an Agent is created, it takes an agent config (model, provider, which MCP servers to use, system prompt) and initializes the conversation history with the system prompt. The load_tools() method then iterates through the server configurations (defined in agent.json) and calls add_mcp_server (from the parent MCPClient) for each one, populating the agent's toolbox.
# Lines 12-54 of `Agent`
# https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_mcp/agent.py#L12:L54
class Agent(MCPClient):
def __init__(
self,
*,
model: str,
servers: Iterable[Dict], # Configuration for MCP servers
provider: Optional[PROVIDER_OR_POLICY_T] = None,
api_key: Optional[str] = None,
prompt: Optional[str] = None, # The system prompt
):
# Initialize the underlying MCPClient with model, provider, etc.
super().__init__(model=model, provider=provider, api_key=api_key)
# Store server configurations to be loaded
self._servers_cfg = list(servers)
# Start the conversation with a system message
self.messages: List[Union[Dict, ChatCompletionInputMessage]] = [
{"role": "system", "content": prompt or DEFAULT_SYSTEM_PROMPT}
]
async def load_tools(self) -> None:
# Connect to all configured MCP servers and register their tools
for cfg in self._servers_cfg:
await self.add_mcp_server(cfg["type"], **cfg["config"])
2. The agent’s core: the Loop
The Agent.run() method is an asynchronous generator that processes a single user input. It manages the conversation turns, deciding when the agent's current task is complete.
# Lines 56-99 of `Agent.run()`
# https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_mcp/agent.py#L56:L99
async def run(self, user_input: str, *, abort_event: Optional[asyncio.Event] = None, ...) -> AsyncGenerator[...]:
...
while True: # Main loop for processing the user_input
...
# Delegate to MCPClient to interact with LLM and tools for one step.
# This streams back LLM text, tool call info, and tool results.
async for item in self.process_single_turn_with_tools(
self.messages,
...
):
yield item
...
# Exit Conditions
# 1. Was an "exit" tool called?
if last.get("role") == "tool" and last.get("name") in {t.function.name for t in EXIT_LOOP_TOOLS}:
return
# 2. Max turns reached or LLM gave a final text answer?
if last.get("role") != "tool" and num_turns > MAX_NUM_TURNS:
return
if last.get("role") != "tool" and next_turn_should_call_tools:
return
next_turn_should_call_tools = (last_message.get("role") != "tool")
Inside the run() loop:
- It first adds the user prompt to the conversation.
- Then it calls
MCPClient.process_single_turn_with_tools(...)to get the LLM's response and handle any tool executions for one step of reasoning. - Each item is immediately yielded, enabling real-time streaming to the caller.
- After each step, it checks exit conditions: if a special "exit loop" tools was used, if a maximum turn limit is hit, or if the LLM provides a text response that seems final for the current request.
Next Steps
There are a lot of cool ways to explore and expand upon the MCP Client and the Tiny Agent 🔥 Here are some ideas to get you started:
- Benchmark how different LLM models and inference providers impact agentic performance: Tool calling performance can differ because each provider may optimize it differently. You can find the list of supported providers here.
- Run tiny agents with local LLM inference servers, such as llama.cpp, or LM Studio.
- .. and of course contribute! Share your unique tiny agents and open PRs in tiny-agents/tiny-agents dataset on the Hugging Face Hub.
Pull requests and contributions are welcome! Again, everything here is open source! 💎❤️
