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

Annotations already mark readOnly/openWorld/idempotent, but the description adds substantial behavior beyond that: the return shape {answer, evidence, confidence, source, fetched_at, refusal_reason:null}, the explicit refusal mechanism with specific refusal_reason values, and the constraint that the answer is extracted 'using ONLY what the tool result contains'. It also discloses the extra LLM call cost and does not contradict 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 each sentence carries purpose: first the core value proposition, then the return/refusal contract, then clear usage guidance and cost tradeoff. The phrase 'Same routing as ask_pipeworx — picks the right tool from 5,743 across 1500 sources, fills arguments, fetches the data' is slightly verbose but reinforces the behavior without padding.

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?

Despite having no output schema, the description covers both success and failure outcomes in detail, including every refusal_reason enum value. For a tool with a single required parameter and rich annotations, nothing an agent needs to invoke it correctly or interpret its results is missing. The tradeoff vs ask_pipeworx is clearly stated.

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 all six parameters are aliases for the same natural-language 'question'. The description does not add much beyond what the schema already provides for parameters, but it does clarify that the question will be routed through the same tool-selection pipeline as ask_pipeworx. This is adequate for a single-logical-parameter tool, so the baseline 3 applies.

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-resource combination: 'Hallucination-resistant answer mode for high-stakes reads' and clearly distinguishes it from ask_pipeworx by describing the extraction-only-answer behavior. It also explicitly names its sibling 'ask_pipeworx' as the same routing but without the grounded extraction, so an agent can tell them apart without opening schemas.

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?

It gives explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with concrete scenarios like 'financial verdicts, legal claims, medical lookups, public statements'. It also provides a clear when-not-to-use: 'prefer ask_pipeworx for casual lookups', citing the extra LLM call cost. This fully routes the agent to the correct sibling.

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