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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds substantial context: the exact return structure with evidence and confidence, explicit refusal reasons, and the extra LLM call cost. This goes well beyond annotations and fully informs the agent of behavioral expectations.

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?

Two sentences, both information-dense. The first sentence front-loads the core value proposition and routing behavior; the second covers return format, refusal cases, usage context, and cost trade-off. No wasted words.

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?

Despite no output schema, the description fully explains the return object and failure modes. It also covers routing, cost, and appropriate use cases. For a tool of this complexity, the description is complete and self-contained.

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% with descriptions for each alias, so the baseline is 3. The description adds no parameter-specific semantics beyond what the schema provides; it only mentions 'question' generically. The aliases are already documented.

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 states the tool's purpose: a hallucination-resistant answer mode that extracts answers only from tool results. It distinguishes from ask_pipeworx by highlighting the refusal behavior and evidence extraction, making its unique value obvious.

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?

Explicitly specifies when to use: high-stakes reads where answers will be quoted, cited, or acted on, with concrete examples. Also provides a clear exclusion: prefer ask_pipeworx for casual lookups due to extra LLM cost. Names the alternative directly.

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