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

The description goes far beyond the annotations (readOnly, openWorld, idempotent, non-destructive). It discloses the full behavioral contract: the success return shape {answer, evidence, confidence, source, fetched_at, refusal_reason:null}, the complete enumeration of refusal reasons (not_in_source, no_tool_match, tool_error, data_truncated, llm_error), the explicit-refusal behavior when data doesn't directly answer, and the extra LLM call cost. No contradiction with 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 long (~120 words) but every sentence earns its place: purpose, mechanics, success return shape, refusal contract, when-to-use, and cost tradeoff — all in a logical flow. It is front-loaded with the most decision-relevant information. The length is justified because there is no output schema, so the return and refusal shapes must be carried in prose.

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 absence of an output schema, the description fully compensates by documenting both the success response and the refusal response with all enum values. It covers routing behavior, the distinction from ask_pipeworx, the usage conditions, and the cost tradeoff. Nothing an agent needs to invoke it correctly or parse its response 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%, so the baseline is 3 per the rubric. The schema already documents the question parameter with all six aliases (query, q, prompt, text, input) and says 'Your question in natural language.' The description adds only indirect context ('fills arguments') and doesn't provide parameter syntax or format details beyond what the schema covers. At baseline, this is acceptable.

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 a specific verb-resource pairing: 'Hallucination-resistant answer mode for high-stakes reads' — a grounded question-answering mode. It clearly differentiates from the sibling ask_pipeworx by naming the 'Same routing' shared mechanic and the key differentiator: extracting the answer using ONLY what the tool result contains. An agent can distinguish this from ask_pipeworx, ask_pipeworx_beta, and bet_research 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?

Explicit when-to-use is stated: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' with concrete examples (financial verdicts, legal claims, medical lookups, public statements). It also names the alternative directly and gives the exclusion condition: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.' This is textbook routing guidance.

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