Entity Enricher

Answer delegated LLM request

answer_llm_request

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: '', 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.

Input Schema

Name	Required	Description
`job_id`	Yes	Job whose llm_requests you are answering.
`response`	Yes	MCP CreateMessageResult: role 'assistant', model (your model's name), stopReason, content (one block or a list of text / tool_use blocks); optional usage {input_tokens, output_tokens}.
`request_id`	Yes	The request_id of the entry you answer.
`wait_seconds`	No	How long to wait for the job's next pending request, pause or completion before returning (0 = return right after handing over the answer).

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 22, 2026

TDQS

A5/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations say this mutates state, is open-world, and is non-destructive, but the description adds critical behavior beyond them: the platform validates the answer like a provider, rejected answers return as new requests with tool_result errors, and the tool may return pending requests, a terminal result, or running status depending on wait_seconds.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Although dense, the paragraph is front-loaded with the core task and then layers in response shape, validation behavior, return semantics, and exclusions. Every sentence carries operational information needed to invoke the tool correctly, with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has a complex nested response object and an output schema, but the description still explains return values and validation loops. Combined with full schema coverage and annotations, it gives an agent everything needed to call and recover from errors correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is already 100%, and the description still adds substantial meaning: it specifies the exact response shape, explains that toolChoice.mode 'required' means one matching tool_use block, and clarifies that wait_seconds controls waiting for the next pending request, pause, or completion.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: answer one delegated LLM request by running it on the caller's own model and passing back an MCP CreateMessageResult-shaped response. It explicitly distinguishes this from question pauses via answer_job_question, so the agent can differentiate it from nearby siblings without inspecting schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit context: use this for every start tool's default unless use_platform_models=true or an explicit platform model is configured. It also names the exclusion ('Not for question pauses') and points to answer_job_question, making the when-to-use boundary clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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