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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses the exact return contract: success shape with evidence and confidence, plus all refusal_reason values. It also surfaces the extra LLM call cost and the restriction that answers are derived solely from fetched tool results. These are meaningful behavioral details not present in the 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 every sentence earns its place: routing, extraction constraint, return shape, refusal reasons, use cases, and cost. It front-loads the core value proposition and ends with a practical cost-based routing hint. No filler or repetition.

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 present, the description compensates fully by documenting both success and refusal response structures. It also names all refusal reasons, covers cost, and provides explicit usage boundaries. An agent has everything needed to invoke this tool correctly and interpret its result.

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 schema fully documents the single question-like parameter and its aliases. The description does not need to add parameter details, and it does not, which is acceptable under the baseline for full schema coverage. It adds no param semantics beyond the schema, hence the mid score.

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 names a specific, distinctive behavior: a hallucination-resistant answer mode that extracts answers only from tool results and returns explicit refusals when data is insufficient. It clearly differentiates from the sibling ask_pipeworx by describing the grounded extraction and refusal semantics. The verb 'extracts' and resource 'answer mode' make the tool's role immediately identifiable.

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?

Provides explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also gives a concrete when-not-to-use directive: 'prefer ask_pipeworx for casual lookups.' The cost trade-off ('one extra LLM call') strengthens the routing decision.

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