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

Annotations declare read-only, open-world, and idempotent behavior; the description adds substantial context beyond that: it guarantees refusal when data is insufficient, enumerates refusal_reason values, and discloses the extra LLM call cost. This meaningfully surpasses what annotations alone convey.

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 but every sentence earns its place: it front-loads the core behavior, then details routing, return structure, refusal semantics, use cases, and cost. Nothing is redundant with the schema, and the structure leads with the most decision-relevant information.

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?

There is no output schema, so the description compensates by specifying the success and refusal return shapes, including the exact refusal_reason enum values. It also covers routing mechanism, intended use cases, and cost tradeoffs, leaving no critical gap for an agent deciding whether and how to call 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 coverage is 100%, so the baseline is 3. The description adds value by clarifying that the single question parameter drives routing across thousands of tools and argument filling, which helps the agent understand how specific the input should be and what will happen with it.

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 clearly identifies a specific mode — hallucination-resistant, grounded answering — and explains that it extracts answers only from fetched tool results. It also names the sibling ask_pipeworx and contrasts this tool's grounded behavior, making differentiation easy.

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 explicitly states when to use this tool: whenever the answer will be quoted, cited, or acted on, and when facts must not be invented. It also gives an exclusion: prefer ask_pipeworx for casual lookups, backed by the cost difference.

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