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

The description goes well beyond the annotations by revealing the exact success return shape, the refusal reason enum, the constraint that answers come only from tool results, and the extra LLM call cost. Annotations already mark it read-only, idempotent, open-world, and non-destructive, and the description adds rich behavioral context without contradicting them.

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 earns its place: it states the mode's purpose, explains the routing/extraction behavior, specifies the return payload, gives concrete usage guidance, and flags the extra cost. It is front-loaded with the key distinction and remains structured and readable.

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 lacking an output schema, the description fully explains return values on both success and refusal, including the refusal_reason enum. It also gives enough contextual information about when to prefer the sibling tool. For a single-parameter grounded question-answering tool, nothing important 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 already documents the question parameter and its aliases. The description does not need to add much parameter-level detail. It doesn't, but the baseline of 3 applies because the schema carries the full burden and the description adds no meaningful parameter semantics beyond that.

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 identifies this as a hallucination-resistant answer mode for high-stakes reads, with a specific behavior: it routes like ask_pipeworx, extracts the answer only from tool results, and returns an explicit refusal if the data doesn't answer. It also distinguishes itself from ask_pipeworx by emphasizing grounded extraction and evidence. This is far more specific than a generic verb+resource statement.

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 explicitly states when to use this tool: when an answer will be quoted, cited, or acted on and the agent must not invent facts. It also names the alternative, ask_pipeworx, and says to prefer it for casual lookups because this mode costs an extra LLM call. This gives the agent clear decision criteria.

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