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

Annotations already establish readOnly/openWorld/idempotent, and the description complements them with rich behavioral detail: it refuses rather than guesses, returns verbatim evidence, enumerates exact refusal reasons (not_in_source, no_tool_match, tool_error, data_truncated, llm_error), and discloses the extra LLM call cost. It also states 'using ONLY what the tool result contains', making the grounding constraint explicit.

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 longer than average but every section earns its place: identity, mechanism, return shape, refusal reasons, usage guidance, and cost tradeoff. It is front-loaded with the core identity and well-organized. Slight extra detail like '5,743 across 1500 sources' is contextually useful but not strictly load-bearing.

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 grounded-answer tool with no output schema, the description fully covers what an agent needs: success response shape, refusal contract, when to use it, and how it differs from ask_pipeworx. The behavioral contract is self-contained; an agent can invoke and interpret the result correctly without external context.

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%, with the sole real parameter `question` fully documented including aliases. The tool description adds no additional parameter-level semantics, but at this coverage level the schema carries the burden. Baseline 3 is appropriate.

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 'Hallucination-resistant answer mode for high-stakes reads', a specific purpose that distinguishes it from the plain ask_pipeworx sibling. It then details the mechanism: 'picks the right tool... fetches the data... then EXTRACTS the answer using ONLY what the tool result contains.' This clearly states what the tool does and differentiates it from siblings.

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 explicitly says 'Use whenever an answer will be quoted, cited, or acted on' and gives concrete high-stakes domains (financial verdicts, legal claims, medical lookups, public statements). It also names the alternative and tradeoff: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.' This is textbook when-to-use/when-not-to-use guidance.

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