Find agent skills by outcome

const truncatedData = data.slice(0

Math.max(1

const data = await apiRequest(params.limit

params.offset);

Data

- Return complete

structured data suitable for programmatic processing

textContent = JSON.stringify(output

null

content: [{ type: text

text: textContent }]

Communication

email: string

// Email address

This tool searches across all user profiles in the Example platform

supporting partial matches and various search filters. It does NOT create or modify users

Communication

description: `Search for users in the Example system by name

│ └── constants.ts # Shared constants (API_URL

CHARACTER_LIMIT

- DO NOT use: Old deprecated APIs such as `server.tool()`

`server.setRequestHandler(ListToolsRequestSchema

content: [{ type: text

text: JSON.stringify(output) }]

<question>Look for pull requests that modified files in the /api directory and were merged between January 1 and January 31

2024. How many different contributors worked on these PRs?</question>

After inspecting the content

create 10 human-readable questions:

- Goal: identify specific content (e.g.

users

After understanding the API and tools

USE the MCP server tools:

- Questions must be designed so there is a single

clear answer

- Look at old content (e.g.

conversations that have ended

Communication

- Email address

phone number

return { content: [{ type: text

text: result }] };

- Store API keys in environment variables

never in code

Data

- Return pagination metadata: Include `has_more`

`next_offset`/`next_cursor`

3. Be action-oriented: Start with verbs (get

list

- Python: `{service}_mcp` (e.g.

`slack_mcp`)

- MCP Protocol: Start with sitemap at `https://modelcontextprotocol.io/sitemap.xml`

then fetch specific pages with `.md` suffix