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

Annotations already mark this read-only and idempotent, but the description adds substantial behavioral context: answers are extracted only from tool results, success returns evidence and confidence, and failures produce explicit refusal_reason values. It also discloses the extra LLM call cost, all of which goes well beyond the annotation hints.

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 carries new information: groundedness, routing reuse, return shape, refusal modes, use cases, and cost tradeoff. The most decision-relevant facts appear first, and the sibling comparison is placed where it matters most, near the end.

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 complex routing tool with one required parameter and no output schema, the description fully covers return values, refusal behavior, when to invoke it, and when to avoid it. Nothing an agent needs to decide whether to call this tool and interpret its result 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?

The schema already documents the single required 'question' parameter at 100% coverage, including all five aliases and the natural-language expectation. The description does not add parameter-level details beyond saying the tool 'fills arguments' during routing, so with this high schema coverage 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 precise verb and resource: a 'hallucination-resistant answer mode for high-stakes reads' that routes through the same machinery as ask_pipeworx and extracts answers only from tool results. It clearly differentiates itself from the sibling ask_pipeworx by emphasizing grounded extraction and explicit refusals, so an agent can distinguish them 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?

It explicitly says when to use this tool ('whenever an answer will be quoted, cited, or acted on... financial verdicts, legal claims, medical lookups, public statements') and when not to ('prefer ask_pipeworx for casual lookups'). The cost tradeoff ('one extra LLM call') and routing relationship to ask_pipeworx give an agent clear selection criteria.

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