FastMCP Cloud MCP Server Builder

Build production-ready MCP servers with FastMCP 2 and deploy them to fastmcp.cloud.

---

Quick Start Workflow

1. Create Project Structure

my-mcp-server/
├── server.py          # FastMCP server (single file)
├── pyproject.toml     # Dependencies
├── .env               # Local secrets (never commit)
├── .gitignore         # Exclude .env and venv
└── README.md          # Usage documentation

2. Minimal Server Template

"""MCP Server for [Service Name]."""

import os
from fastmcp import FastMCP

mcp = FastMCP(
    "my-service-mcp",
    instructions="Description of what this server does and how to use it.",
)

@mcp.tool()
async def my_tool(param: str) -> str:
    """Tool description for LLM discovery."""
    return f"Result: {param}"

if __name__ == "__main__":
    host = os.getenv("MCP_HOST", "0.0.0.0")
    port = int(os.getenv("MCP_PORT", "8000"))
    mcp.run(transport="http", host=host, port=port)

3. Deploy to fastmcp.cloud

1. Push to GitHub (public or private repo)
2. Visit fastmcp.cloud and authenticate with GitHub
3. Create project, set entrypoint to

   server.py

4. Server deploys to

   https://your-project.fastmcp.app/mcp

---

Development Process

Phase 1: Setup

Install dependencies with uv:

uv venv
uv pip install "fastmcp>=2.0.0,<3" httpx pydantic python-dotenv

Create pyproject.toml:

[project]
name = "my-mcp-server"
version = "0.1.0"
description = "MCP server for [service]"
requires-python = ">=3.11"
dependencies = [
    "fastmcp>=2.0.0,<3",
    "httpx>=0.27.0",
    "pydantic>=2.0.0",
]

Create .gitignore:

.env
.venv/
venv/
__pycache__/
*.pyc

Phase 2: Implement Tools

Load the FastMCP Patterns Guide for detailed implementation patterns.

Key patterns:

• Use

  @mcp.tool()

  decorator for all tools
• Use Pydantic models or type hints for validation
• Support both JSON and Markdown response formats
• Implement pagination for list operations
• Use async/await for all I/O operations

Phase 3: Test Locally

# Activate virtual environment
source .venv/bin/activate

# Load environment variables
export $(cat .env | xargs)

# Run server locally
python server.py

# Or test with MCP Inspector
fastmcp inspect server.py

Phase 4: Deploy

See Deployment Guide for complete deployment instructions.

---

Reference Documentation

FastMCP Patterns

• FastMCP Patterns Guide - Tool registration, Pydantic models, response formats

Deployment

• Deployment Guide - fastmcp.cloud deployment, secrets, CI/CD

External Resources

• FastMCP Documentation MCP Server: Connect to

  https://gofastmcp.com/mcp

  for live documentation access
• FastMCP Docs: gofastmcp.com
• MCP Protocol: modelcontextprotocol.io

---

Example Server

See example/server.py for a complete, deployable MCP server template that demonstrates all best practices.

---

Tool Naming Convention

Use the pattern

{service}_{action}_{resource}

:

• github_list_repos

• slack_send_message

• db_query_users

Always include service prefix to avoid conflicts when multiple MCP servers are used together.

---

Response Format Pattern

Support both JSON (for programmatic use) and Markdown (for human readability):

from enum import Enum

class ResponseFormat(str, Enum):
    MARKDOWN = "markdown"
    JSON = "json"

@mcp.tool()
async def list_items(
    limit: int = 20,
    response_format: str = "json"
) -> str:
    items = await fetch_items(limit)

    if response_format == "markdown":
        lines = ["# Items", ""]
        for item in items:
            lines.append(f"- **{item['name']}**: {item['description']}")
        return "\n".join(lines)

    return json.dumps({"count": len(items), "items": items}, indent=2)

---

Error Handling Pattern

def handle_api_error(e: Exception) -> str:
    """Consistent error formatting."""
    if isinstance(e, httpx.HTTPStatusError):
        status = e.response.status_code
        if status == 401:
            return "Error: Authentication failed. Check API credentials."
        elif status == 404:
            return "Error: Resource not found."
        elif status == 429:
            return "Error: Rate limit exceeded. Wait before retrying."
        return f"Error: API request failed with status {status}"
    return f"Error: {type(e).__name__}: {e}"

---

Secrets Management

Local development:

• Store secrets in

  .env

  file
• Load with

  python-dotenv

  or export manually
• Never commit

  .env

  to git

fastmcp.cloud:

• Use project settings to configure environment variables
• Secrets are encrypted and injected at runtime
• Access via

  os.environ.get("SECRET_NAME")

See Deployment Guide for detailed secrets management instructions.