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Build MCP servers in Python with FastMCP. Define tools / resources / prompts, build the server, test locally, deploy to FastMCP Cloud or Docker.
Passed 22 of 23 test cases with the skill, and 19 of 23 without it.
and 1 more in the full report
3 fixed19 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
A newer attempt on gemini-3.6-flash on 10/2/2026 did not produce a publishable comparison (not comparable). The snapshot above is the previously measured run.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
Inside an MCP tool function, I need to send a prompt back to the host LLM to generate a text summary before returning. What context method performs this request?
| Checked | Without skill | With skill |
|---|---|---|
| Calls context.request_sampling with message parameters | Missed | Passed |
The instructions your model reads when the skill loads.
Build a working MCP server from a description of the tools you need. Produces a deployable Python server using FastMCP.
Ask what the server needs to provide:
A brief like "MCP server for querying our customer database" is enough.
pip install fastmcp
Create the server file. The server instance MUST be at module level:
from fastmcp import FastMCP
# MUST be at module level for FastMCP Cloud
mcp = FastMCP("My Server")
@mcp.tool()
async def search_customers(query: str) -> str:
"""Search customers by name or email."""
# Implementation here
return f"Found customers matching: {query}"
@mcp.resource("customers://{customer_id}")
async def get_customer(customer_id: str) -> str:
"""Get customer details by ID."""
return f"Customer {customer_id} details"
test cases passed, vs 19 / 23 without
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