Answer one LLM request of a job running on your model (every start tool's default unless use_platform_models=true or an explicit platform model): you are its model. Read the request under get_job_status(job_id).llm_requests (or in the start tool's return): run systemPrompt + messages with the listed tools and toolChoice on your own model, then pass the result here as response in MCP CreateMessageResult shape — {role: 'assistant', model: '<your model>', stopReason: 'toolUse' | 'endTurn', content: [{type: 'tool_use', id, name, input}] | {type: 'text', text}} — with optional usage {input_tokens, output_tokens}. toolChoice.mode 'required' means one tool_use block whose input matches the tool's inputSchema. The platform validates the answer like a provider's: a rejected answer returns as a new request with the error in a tool_result (isError) — fix and answer again. Returns the next pending request(s), the terminal result, or the running status after wait_seconds. Not for question pauses (answer_job_question). No platform LLM call. Guide: enricher://docs/llm-delegation.