FastMCP: Build Production-Ready MCP Servers in Python with Minimal Boilerplate

Exploring Your Server: Tools, Resources, and Prompts

FastMCP supports three core primitives.

Tools — Executable functions the LLM can call.

Resources — Readable data (static or dynamic) the LLM can fetch.

Prompts — Reusable prompt templates with parameters.

Add these to the same file:

from typing import List, Dict

import datetime



# Dynamic resource example

@mcp.resource("resource://time/now")

def current_time() -> str:

    """Return current UTC time as ISO string."""

    return datetime.datetime.now(datetime.timezone.utc).isoformat()



# List resource

@mcp.resource("resource://users")

def list_users() -> List[Dict]:

    """Return a list of sample users. In production, query your DB here."""

    return [

        {"id": 1, "name": "Alice", "role": "engineer"},

        {"id": 2, "name": "Bob", "role": "designer"}

    ]



# Prompt template

@mcp.prompt

def research_topic(topic: str, depth: str = "medium") -> str:

    """Generate a structured research prompt."""

    return f"""

    Research {topic} at {depth} depth.

    1. Use available tools to gather latest information.

    2. Summarize key findings.

    3. List open questions.

    """

These are automatically discoverable. Resources are great for live data (JIRA tickets, GitHub issues, database views) without forcing the LLM to call a tool every time.

Connecting Clients and Testing Locally

FastMCP includes a rich client library. Create client.py:

import asyncio

from fastmcp import Client



async def main():

    async with Client("http://localhost:8000") as client:  # Adjust URL/port

        # List capabilities

        tools = await client.list_tools()

        print("Available tools:", [t.name for t in tools])



        # Call a tool

        result = await client.call_tool("add", {"a": 15, "b": 27})

        print("15 + 27 =", result)



        # Read a resource

        time_data = await client.read_resource("resource://time/now")

        print("Current time:", time_data)



asyncio.run(main())

For local development with interactive UIs, use the built-in dev server (more on that below).

Building Interactive Apps Inside the Conversation

One of FastMCP’s most powerful features is Apps — tools that return rich, interactive UIs rendered directly in the host’s conversation.

Mark a tool with app=True and return Prefab components (or custom HTML).

Example dashboard.py:

from fastmcp import FastMCP

from prefab import Column, Header, Chart, DataTable, Button, CallTool  # Prefab components



mcp = FastMCP("sales-demo")



def fetch_sales_data(region: str):

    # Simulate or query real data

    return [

        {"month": "Jan", "revenue": 12000},

        {"month": "Feb", "revenue": 15000},

        # ...

    ]



@mcp.tool(app=True)

def sales_dashboard(region: str = "Global"):

    """Interactive sales dashboard for the selected region."""

    data = fetch_sales_data(region)



    return Column([

        Header(f"Sales Dashboard - {region}"),

        Chart(data, type="bar", title="Monthly Revenue"),

        DataTable(data, searchable=True),

        Button(

            "Export CSV",

            on_click=CallTool("export_sales", {"region": region})

        )

    ])



# Backend tool used by the UI (hidden from LLM by default)

@mcp.tool

def export_sales(region: str):

    # Generate and return file or link

    return {"status": "success", "message": f"CSV for {region} ready"}

Run with fastmcp dev apps dashboard.py to preview locally. The dev UI gives you a picker, auto-generated forms, live rendering, and an MCP inspector showing all traffic. Changes hot-reload.

Advanced Patterns and Production Considerations

Server Composition & Namespacing

from fastmcp import FastMCP, Provider



main_mcp = FastMCP("main")



# Mount another server under a prefix

github_provider = ...  # or another FastMCP instance

main_mcp.mount(github_provider, prefix="github")

Authentication & Security

FastMCP supports multiple auth methods. You keep credentials server-side; the LLM only sees tool results. This is a major advantage over exposing raw API keys or full shell access.

Deployment

• Run locally or on any VPS with mcp.run().


• For production, Prefect Horizon offers managed hosting, GitHub-based deploys, branch previews, SSO, RBAC, audit logs, etc.


• Containerize easily with Docker for self-hosting.

Debugging Tips

• Use fastmcp dev apps for UI tools.


• Append .md to any docs URL on gofastmcp.com for markdown.


• The server at https://gofastmcp.com/mcp lets you query the docs via MCP itself.

Common Real-World Use Cases

• 
  Internal Tools: Expose company DB queries, ticket systems, or monitoring data safely.


• 
  Personal Agents: Connect to your calendar, email summaries, or note-taking app.


• 
  Data Analysis: Dynamic resources for CSVs, live API feeds, or vector search results.


• 
  Workflow Automation: Tools that trigger Prefect flows or other orchestration.


• 
  E-commerce / CRM Demos: As seen in community examples with order management and dashboards.

Next Steps and Resources

1. Read the official quickstart and tutorials at
   
   
   

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