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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description goes far beyond: it discloses the full refusal contract with five explicit refusal_reason codes, the exact success return shape ({answer, evidence, confidence, source, fetched_at, refusal_reason:null}), and the constraint that answers are extracted 'using ONLY what the tool result contains.'

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?

Roughly 140 words carry the value proposition, routing mechanism, both return contracts, five refusal reasons, when-to-use guidance, and the cost tradeoff against ask_pipeworx. Every sentence earns its place and the key differentiator ('Hallucination-resistant') is front-loaded.

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?

With no output schema, the description carries full responsibility for return-value disclosure — and it does so completely for both the success and refusal paths, including the refusal_reason enumeration. Combined with annotations covering the safety profile and a single required parameter documented in the schema, nothing an agent needs to call 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 schema already documents `question` and all five aliases (q, text, input, query, prompt) with descriptions. The description adds no parameter-level detail beyond the fact that the input is a natural-language question, so the high-coverage baseline of 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?

States a specific value proposition: 'Hallucination-resistant answer mode for high-stakes reads.' It names the mechanism ('picks the right tool from 5,724 across 1497 sources... then EXTRACTS the answer using ONLY what the tool result contains') and differentiates itself from the sibling ask_pipeworx ('Same routing as ask_pipeworx'), 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?

Explicitly instructs when to use: '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 names the alternative and the condition that selects it: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.'

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