Find agent skills by outcome

result = await evaluate_single_task(client

model

Productivity

When given a task

you MUST:

Before finalizing your Python MCP server implementation

ensure:

- [ ] Tools enable complete workflows

not just API endpoint wrappers

return await api_call(resource_id

api_key)

- `ctx.log_info(message

data)` / `ctx.log_error()` / `ctx.log_debug()` - Logging

Productivity

FastMCP can automatically inject a `Context` parameter into tools for advanced capabilities like logging

progress reporting

'''Search for users in the Example system by name

async def _make_api_request(endpoint: str

method: str = GET

query: str = Field(...

description=Search string to match against names/emails

email: str

# Email address (e.g.

This server provides tools to interact with Example API

including user search

- query (str): Search string to match against names/emails (e.g.

john

Search for users in the Example system by name

async def _make_api_request(endpoint: str

method: str = GET

data = await api_request(limit=params.limit

offset=params.offset)

def validate_email(cls

v: str) -> str:

email: str = Field(...

description=User's email address

The official MCP Python SDK provides FastMCP

a high-level framework for building MCP servers. It provides:

This document provides Python-specific best practices and examples for implementing MCP servers using the MCP Python SDK. It covers server setup

tool registration patterns

- [ ] Tools enable complete workflows

not just API endpoint wrappers

* This server provides tools to interact with Example API

including user search

const data = await apiRequest(params.limit

params.offset);

email: string

// Email address