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

Even with annotations covering read-only and idempotent hints, the description adds substantial behavioral detail: same routing as ask_pipeworx, argument-filling and data-fetching pipeline, extraction limited to tool results, explicit success return shape, and a detailed refusal schema with specific refusal reasons. This goes well beyond what annotations provide and contradicts nothing.

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: purpose, mechanism, return contract, refusal contract, when to use, and cost trade-off. It is front-loaded with the most decision-relevant fact (hallucination-resistant) and flows logically from selection to invocation to outcome.

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 fully specifies the return shape on success and the refusal shape with enumerated refusal reasons. It also covers routing scope, evidence format, cost implications, and usage context, so an agent has everything needed to call and interpret the result correctly.

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 the input schema already documents the question parameter and all aliases. The description does not add new parameter-level semantics, but the baseline of 3 is appropriate because the schema carries the full burden and the description's focus is appropriately on behavior and usage rather than repeating schema details.

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, differentiated purpose: it is a 'hallucination-resistant answer mode for high-stakes reads' that extracts the answer strictly from fetched tool results. It explicitly contrasts itself with ask_pipeworx, making the distinction between sibling tools clear without requiring schema inspection.

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 ('whenever an answer will be quoted, cited, or acted on', high-stakes domains) and when-not-to-use guidance ('prefer ask_pipeworx for casual lookups'). It also names the main alternative and explains the cost trade-off of one extra LLM call.

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