MCP Python Starter
About
A feature-complete Model Context Protocol (MCP) server template written in Python using FastMCP. It demonstrates all major MCP features—tools, resources, templates, and prompts—with clean, Pythonic code, serving as a jumpstart for developers building their own MCP servers.
Details
- License
- MIT
Explore
| Category | Feature | Description |
|----------|---------|-------------|
| Tools | hello | Basic tool with annotations |
| | get_weather | Tool returning structured data |
| | ask_llm | Tool that invokes LLM sampling |
| | long_task | Tool with 5-second progress updates |
| | load_bonus_tool | Dynamically loads a new tool |
| Resources | info://about | Static informational resource |
| | file://example.md | File-based markdown resource |
| Templates | greeting://{name} | Personalized greeting |
| | data://items/{id} | Data lookup by ID |
| Prompts | greet | Greeting in various styles |
| | code_review | Code review with focus areas |
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
MCP Python StarterCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
- Python 3.11+
- uv (recommended) or pip
uv sync
stdio transport (for local development):
uv run mcp-python-starter --stdio
HTTP transport (for remote/web deployment):
uv run mcp-python-starter --http --port 3000
npx @modelcontextprotocol/inspector -- uv run mcp-python-starter
Copy .env.example to .env and configure:
cp .env.example .env
@mcp.tool(
title="Say Hello",
description="A friendly greeting tool",
annotations={"readOnlyHint": True},
)
def hello(name: str) -> str:
"""Say hello to someone.
Args:
name: The name to greet
"""
return f"Hello, {name}!"
@mcp.tool(title="Long Task")
async def long_task(
task_name: str,
ctx: Context[ServerSession, None],
) -> str:
for i in range(5):
await ctx.report_progress(
progress=i / 5,
total=1.0,
message=f"Step {i + 1}/5",
)
await asyncio.sleep(1.0)
return "Done!"
@mcp.tool(title="Ask LLM")
async def ask_llm(
prompt: str,
ctx: Context[ServerSession, None],
) -> str:
result = await ctx.session.create_message(
messages=[{"role": "user", "content": {"type": "text", "text": prompt}}],
max_tokens=100,
)
return result.content.text
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"mcp python starter": {
"mcp-python-starter": {
"command": "uv",
"args": [
"sync"
]
}
}
}
}McpServers
{
"mcp-python-starter": {
"command": "uv",
"args": [
"sync"
]
}
}
A feature-complete Model Context Protocol (MCP) server template in Python using FastMCP. This starter demonstrates all major MCP features with clean, Pythonic code.
📚 Documentation
- Model Context Protocol
- Python SDK
- FastMCP Guide
✨ Features
| Category | Feature | Description |
|----------|---------|-------------|
| Tools | hello | Basic tool with annotations |
| | get_weather | Tool returning structured data |
| | ask_llm | Tool that invokes LLM sampling |
| | long_task | Tool with 5-second progress updates |
| | load_bonus_tool | Dynamically loads a new tool |
| Resources | info://about | Static informational resource |
| | file://example.md | File-based markdown resource |
| Templates | greeting://{name} | Personalized greeting |
| | data://items/{id} | Data lookup by ID |
| Prompts | greet | Greeting in various styles |
| | code_review | Code review with focus areas |
🚀 Quick Start
Prerequisites
- Python 3.11+
- uv (recommended) or pip
Installation
```bash
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