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

Annotations already mark the tool read-only and idempotent, and the description adds substantial behavioral detail: it only uses tool-result content, returns evidence as a verbatim quote, provides confidence and source, and can return structured refusal reasons such as 'not_in_source' and 'data_truncated'. It also discloses the performance tradeoff of an extra LLM call. No contradictions with 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 dense but every clause earns its place: it states the mode, explains the routing behavior, specifies the success/refusal return contract, lists concrete use cases, and gives a cost-based comparison to the sibling. Information is front-loaded with the core purpose first, and the refusal taxonomy is compactly embedded.

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 having no output schema, the description fully specifies the return shape on success and failure, enumerates all refusal reasons, covers cost implications, and names the sibling alternative. For a tool with one required parameter and rich behavioral guarantees, nothing materially needed to invoke 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 coverage is 100%, with all six parameters documented as aliases for the single natural-language 'question' field. The description reiterates that the tool fills arguments internally and accepts a question, but it does not add new parameter-level meaning beyond the schema. Baseline 3 is appropriate because the schema already carries the burden.

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 immediately identifies the tool as a 'hallucination-resistant answer mode for high-stakes reads' with a specific workflow: route, fill arguments, fetch data, then extract the answer only from the tool result. It clearly distinguishes itself from ask_pipeworx by positioning itself as the grounded variant while sharing the same routing. The purpose is concrete and actionable.

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?

Provides explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also names the alternative and gives a clear exclusion: 'prefer ask_pipeworx for casual lookups' and notes the extra LLM call cost. This fully equips an agent to select between siblings.

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