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

The description discloses concrete behavioral traits: it routes like ask_pipeworx, fetches data, extracts answers strictly from tool results, returns a structured success object, and returns explicit refusal reasons when the data doesn't answer. It adds substantial context beyond the annotations without contradicting them.

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 well-organized, front-loading the core purpose and high-stakes use case before detailing output shape and refusal behavior. Every sentence contributes useful information for selecting and invoking the tool.

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?

Given the tool's complexity, the lack of an output schema, and the availability of sibling ask_pipeworx, the description is complete: it explains what it does, when to use it, what it returns, how it fails, and how it differs from the alternative. An agent has enough context to decide and call correctly.

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%, and the schema already documents 'question' and all aliases with natural language semantics. The description doesn't add much parameter-level detail, but it doesn't need to because the schema carries that burden.

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 states a specific verb and resource: a 'hallucination-resistant answer mode' that extracts answers using only tool results. It clearly distinguishes itself from the sibling ask_pipeworx by emphasizing grounded, evidence-based answers vs casual lookups.

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?

Usage guidance is explicit: use when an answer will be quoted, cited, or acted on, and when inventing facts is unacceptable. It also names the alternative, ask_pipeworx, and says to prefer it for casual lookups because of the extra LLM call cost.

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