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

Goes well beyond the annotations: discloses budget consumption per model (premium up to 20x), monthly vs extra-answer reserves and their reset/expiry rules, the specific ANSWER_RUNS_TOTAL_LIMIT failure condition, the 5 calls/minute rate limit, and asynchronous delivery. Annotations only cover the safety profile, so this adds substantial operational context.

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

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

Front-loaded with the action, then layered with budget, failure, return-shape, async, and rate-limit facts. It is dense and every sentence carries information, though it runs long and could be tightened slightly.

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

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

With no output schema, the description still covers the return shape (answerRuns with used/limit/credits), the async delivery model and where to follow up, the failure mode, and the rate limit. It is complete for a 3-parameter mutation tool.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds meaning beyond the schema: models may be a subset and defaults to every enabled model, and model choice directly affects budget consumption (premium up to 20x). This makes the cost of the models parameter tangible.

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

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

States a specific verb and resource: 'Queue an on-demand run of an ACTIVE prompt across the workspace's enabled AI models'. The scope (active prompts, enabled models, optional subset) is precise enough to distinguish it from siblings like rerun_pitch and create_prompts.

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

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

Establishes the context clearly ('on-demand run of an ACTIVE prompt') and routes the agent to list_chats to retrieve async results. It does not explicitly contrast with alternatives such as get_pitch_status or rerun_pitch, so it stops short of full when/when-not guidance.

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