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

Beyond the readOnly/idempotent annotations, the description discloses important behaviors: it only uses the tool result, includes verbatim evidence, returns explicit refusal reasons, and costs one extra LLM call. This is substantial, non-obvious behavioral context that helps the agent calibrate trust and cost.

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, behavior, return format, refusal reasons, usage guidance, and cost trade-off. It is front-loaded with the most important distinguishing trait and remains readable.

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?

Even without an output schema, the description fully specifies both success and refusal shapes, the key behavioral guarantee, and the selection trade-off against ask_pipeworx. An agent has enough context to call this tool correctly and interpret its results.

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?

The input schema already documents the question parameter and all six aliases with 100% coverage. The description adds no parameter-level semantics, but none are needed because the schema covers the single conceptual parameter thoroughly.

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 purpose: a hallucination-resistant answer mode that extracts answers exclusively from tool results and returns refusals when data does not directly answer. It also distinguishes itself from ask_pipeworx by name and by the grounded-extraction behavior.

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 ('high-stakes reads', quoted/cited/acted-on answers) and explicit when-not-to-use guidance ('prefer ask_pipeworx for casual lookups'). It also names the alternative tool directly, making selection unambiguous.

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