while comprehensive coverage gives agents flexibility to compose operations. Performance varies by client—some clients benefit from code execution that combines basic tools

Mar 20, 2026

Find agent skills by outcome

- Return complete

const data = await apiRequest(params.limit

content: [{ type: text

textContent = JSON.stringify(output

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

- The `register*` methods provide better type safety

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

content: [{ type: text

2. **Load each task instruction** and in parallel using the MCP server and tools

Remember to parallelize solving tasks to avoid running out of context

After inspecting the content

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

- Goal: identify specific content (e.g.

After understanding the API and tools

- Look at old content (e.g.

return { content: [{ type: text

- Store API keys in environment variables

- **Return pagination metadata**: Include `has_more`

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

Prioritize comprehensive API coverage. List endpoints to implement

- [🐍 Python Guide](./reference/python_mcp_server.md) - Module organization

- **Language**: TypeScript (high-quality SDK support and good compatibility in many execution environments e.g. MCPB. Plus AI models are good at generating TypeScript code

Review the service's API documentation to identify key endpoints

Balance comprehensive API endpoint coverage with specialized workflow tools. Workflow tools can be more convenient for specific tasks

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

2. Load each task instruction and in parallel using the MCP server and tools

- Return pagination metadata: Include `has_more`

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

- Language: TypeScript (high-quality SDK support and good compatibility in many execution environments e.g. MCPB. Plus AI models are good at generating TypeScript code