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

The description richly discloses behavior beyond the annotations: it returns a structured success object, returns an explicit refusal with enum-like refusal reasons when the data does not answer, and reveals the extra LLM call cost. This is exactly the kind of behavioral context an agent needs, and it does not contradict the readOnlyHint, openWorldHint, or idempotentHint annotations.

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 dense but every sentence earns its place: purpose, success return shape, refusal behavior, when to use, and cost tradeoff are all covered with no filler. The most decision-relevant information is front-loaded in the first sentence.

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 a tool with no output schema, the description fully compensates by specifying the exact return fields and all refusal reason values. It also covers operational guidance, cost tradeoffs, and when to select the sibling alternative. Nothing an agent needs to invoke this correctly is missing.

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 description coverage is 100%: the only real parameter, question, is already fully described in the schema including its aliases. The description adds no additional parameter-level meaning, so the baseline of 3 applies. It does clarify that the tool internally fills arguments, but that is not user-parameter semantics.

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 names a specific mode ('hallucination-resistant answer mode'), explains that it routes like ask_pipeworx but extracts answers only from tool results, and distinguishes itself from the sibling tool ask_pipeworx. An agent can clearly tell what this tool does and how it differs from its closest alternative.

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?

The description gives explicit when-to-use guidance: use whenever the answer will be quoted, cited, or acted on and facts must not be invented, with examples including financial verdicts, legal claims, and medical lookups. It also states when not to use it by noting it costs one extra LLM call and that ask_pipeworx should be preferred for casual lookups.

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