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

Annotations already assert readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description goes well beyond these by disclosing the internal mechanism (routing, argument-filling, fetching, then extraction), the exact success/refusal return contracts, and the list of refusal reasons including data_truncated and llm_error. It also warns about the extra LLM call cost. This is rich behavioral disclosure with no contradiction.

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?

The description is long but every sentence earns its place: it fronts the core purpose, then gives the mechanism, return contract, when-to-use, and cost tradeoff. It is structured and dense rather than padded, but the length is on the higher side for a tool description, so a perfect 5 is not warranted.

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?

Even without an output schema, the description fully details the return shape and all refusal scenarios, which covers the main missing piece. Given the tool's complexity (6 aliased parameters, many siblings, and nuanced tradeoff against ask_pipeworx), the description is nearly complete. Minor omissions like example inputs or rate-limit caveats prevent a 5.

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%: all six parameters are documented as aliases for 'question.' The description adds no parameter-specific detail, but it does not need to because the schema fully explains that any of q, text, input, query, prompt, or question can be used. This matches the baseline of 3 when the schema carries the load.

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?

The description opens with a precise, resource-specific phrase, 'Hallucination-resistant answer mode for high-stakes reads,' and immediately differentiates it from ask_pipeworx by noting 'Same routing as ask_pipeworx ... then EXTRACTS the answer using ONLY what the tool result contains.' This clearly identifies what the tool does and how it differs from its closest sibling without ambiguity.

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?

Explicit when-to-use guidance is given: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements).' It also gives an exclusion and alternative: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.' This is textbook usage guidance.

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