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

Far exceeds the annotations, which only say readOnly/idempotent/openWorld: it discloses the exact success payload shape, the full refusal taxonomy with each refusal_reason value, the refusal_cause sub-semantics for llm_error, and the cost delta of one extra LLM call. This is exactly the behavioral context the annotations cannot carry.

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?

Front-loaded with the core promise, then progressively discloses return shape, refusals, and cost tradeoff. It is dense and the refusal_cause parenthetical is heavy, but every clause carries decision-relevant information rather than filler.

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?

No output schema exists, yet the description fully enumerates both return branches (success payload and refusal payload) with field names and reason values, so an agent knows exactly what to expect. Nothing needed to invoke or interpret this 9-parameter tool is missing.

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, but the description adds real routing semantics: the question is dispatched across 7,136 tools and arguments are filled from it, and prefer_tool forces an exact tool with unknown names refused. It does not restate the alias list, which is fine since the schema does.

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?

States a specific verb and resource ('Hallucination-resistant answer mode' that routes, fetches, then extracts an answer) and immediately contrasts it with its sibling ask_pipeworx. An agent can distinguish this from ask_pipeworx and ask_pipeworx_beta without opening any schema.

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?

Explicit when-to-use ('whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts') with concrete examples, plus an explicit when-not ('prefer ask_pipeworx for casual lookups') justified by the extra LLM call cost. The only gap is that ask_pipeworx_beta is never mentioned.

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