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

Beyond annotations (readOnly, openWorld, idempotent), the description discloses important behavioral traits: it uses the same routing as ask_pipeworx, extracts answers only from tool results, returns a structured success shape with verbatim evidence, and can return explicit refusal reasons. It also reveals the extra LLM call cost and the exact refusal categories, giving the agent an accurate model of behavior.

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 adds value: purpose, routing behavior, return contract, refusal semantics, usage context, cost tradeoff, and alternative. It is front-loaded with the most decision-relevant information and ends with the cost comparison.

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, the description fully specifies both the success shape and failure/refusal shape, including the refusal_reason enum values. It also covers when to use this tool versus ask_pipeworx and mentions the cost consequence, leaving no critical gap for an agent deciding whether to invoke it.

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 already documents all six aliases as the natural-language question. The description adds minimal parameter-level meaning beyond saying 'Your question in natural language' in the schema, which matches the 3 baseline for high schema coverage.

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 identifies a specific mode—'hallucination-resistant answer mode for high-stakes reads'—with a concrete verb (answer) and resource (Pipeworx data). It explicitly distinguishes itself from ask_pipeworx by emphasizing evidence extraction and refusal behavior, so an agent can differentiate it from its siblings without inspecting 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?

The description gives explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' followed by domain examples. It also states when NOT to use it ('prefer ask_pipeworx for casual lookups') and notes the extra LLM call cost as a tradeoff.

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