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

The description goes well beyond the annotations by specifying the exact success return shape: {answer, evidence, confidence, source, fetched_at, refusal_reason:null}, and the full refusal_reason enum: 'not_in_source', 'no_tool_match', 'tool_error', 'data_truncated', 'llm_error.' It also discloses the extra LLM call cost, which is not visible in annotations or schema.

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 long but every sentence earns its place: purpose, routing behavior, grounding guarantee, output schema, refusal reasons, usage cases, and cost tradeoff. The critical differentiating traits are front-loaded, and the refusal semantics are compactly enumerated rather than left ambiguous.

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 with no output schema and a complex routing-and-grounding behavior, the description is remarkably complete. It tells the agent what the tool does, when to use it, what the return looks like in success and failure, whether it is safe/read-only via annotations, and how it compares to the close sibling ask_pipeworx.

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 input schema has 100% coverage: the single required question parameter is fully described, including all aliases. The tool description adds broader context about argument-filling for underlying tools, but it doesn't add new meaning about the question parameter itself beyond what the schema already provides. 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, differentiated purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It clearly states that this tool routes like ask_pipeworx but extracts the answer using ONLY the tool result, making it easy for an agent to distinguish it from ask_pipeworx and other siblings.

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 usage guidance: use when an answer 'will be quoted, cited, or acted on' and when the agent 'must not invent facts.' It also names the alternative, ask_pipeworx, and explains the tradeoff: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.' This fully addresses when to use this tool versus the sibling.

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