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

Beyond the annotations' readOnly/openWorld/idempotent hints, the description reveals the full behavioral contract: the return shape on success, the explicit refusal object with enumerated refusal_reason values, and the 'using ONLY what the tool result contains' guarantee. It also discloses the extra LLM call cost. Nothing here contradicts the annotations.

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 information-dense but every sentence earns its place: it states the mode, the routing mechanism, the guarantee, the return contract, the use cases, and the cost tradeoff. It is front-loaded with the most decision-relevant trait and uses compact examples like the refusal enum list rather than wandering prose.

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?

For a tool of this complexity and with no output schema, the description is unusually complete: it covers routing, grounding behavior, success and failure return shapes, refusal reasons, cost, and selection context. The annotations handle the safety profile, and the schema handles parameters, so nothing an agent needs to call it correctly 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 description coverage is 100% and the schema itself fully documents the aliases and the natural-language 'question' parameter. The description adds no new parameter-level semantics beyond what the schema already provides, so the baseline of 3 is appropriate.

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 opens with a specific function — 'Hallucination-resistant answer mode for high-stakes reads' — and explains exactly what differentiates it from the sibling ask_pipeworx: it routes the same way but extracts the answer using only the tool result. It names the sibling directly and states the edge the grounded mode provides, so an agent can distinguish them without opening the 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?

The description gives explicit when-to-use and when-not-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with concrete domains, then 'prefer ask_pipeworx for casual lookups.' It also transparently mentions the cost tradeoff ('Costs one extra LLM call'), which is exactly the kind of selection criterion an agent needs.

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