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

Annotations already declare readOnly/openWorld/idempotent hints, and the description adds substantial behavioral context: it picks from thousands of sources, fills arguments, fetches data, extracts answers using only the tool result, returns refusal reasons, and costs an extra LLM call. This goes well beyond the structured annotations and fully discloses the tool's execution behavior.

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 core purpose, explains the mechanism, details the success and refusal responses, provides concrete use cases, and gives a cost-based alternative. It is front-loaded with the most important differentiator and remains scannable despite its length.

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 a single question parameter and no output schema, the description is remarkably complete. It documents the success and refusal return structures, lists all refusal reasons, names the sibling alternative, and explains the cost tradeoff. An agent has everything necessary to select and invoke the tool correctly.

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%, with all six parameters documented as aliases for the single 'question' argument. The description does not add further parameter-level detail beyond confirming natural language input, but that is sufficient given the schema already fully covers the only meaningful parameter.

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 states a specific verb and resource: 'hallucination-resistant answer mode' for high-stakes reads, and clearly distinguishes it from ask_pipeworx by emphasizing extraction from the tool result and explicit refusal behavior. It also enumerates the exact return shape, leaving no ambiguity about what the tool does.

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 says when to use this tool ('whenever an answer will be quoted, cited, or acted on') and when not to ('prefer ask_pipeworx for casual lookups'). It names the alternative and provides the key tradeoff (one extra LLM call), giving 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.