Generate Screening Answers

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

Adds real context beyond annotations: requires the user's API key and errors without it, grounded in the resume and linked referral contact, and 409 refusal tied to get_setup_status. Does not mention latency/cost of the LLM batch or batching limits, but covers the main behavioral traits.

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-loads the core purpose and parameter clarification, with backticked parameter names aiding scanability. Dense but nearly every sentence carries routing or behavioral information.

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?

For an open-world, non-idempotent mutation with no output schema, it covers auth prerequisites, grounding sources, refusal conditions, and the sibling alternative. Little is left ambiguous, though return/format expectations are absent.

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?

With 0% schema description coverage, the description usefully clarifies that `questions` is the complete list of questions to answer in this batch, and shows the kind of free-form prompts accepted. `application_id` is left implied as the target application.

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+resource ('writes a batch of screening-question answers for an application') with the actor (Maestro's AI) and scope. It is clearly distinguishable from store_qa_answers, which is described as storing caller-written answers.

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?

Explicitly names the alternative (store_qa_answers for answers the caller wrote, no LLM call) and states a when-not condition (refused 409 when the writer setting says the agent app writes). Lacks a crisp positive 'use when' statement, but the routing is clear.

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