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

Beyond the read-only and idempotent annotations, the description discloses that the tool never invents facts, returns evidence verbatim, and may explicitly refuse with specific reasons if data doesn't directly answer. It also reveals the extra LLM call cost, providing behavioral transparency not captured by 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 somewhat verbose with repetitive phrasing (e.g., 'Same routing as ask_pipeworx' and 'then EXTRACTS'), but it is logically structured, covers return types, refusals, and usage advice. Minor redundancy prevents a perfect score, but it remains efficient overall.

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?

With no output schema, the description effectively compensates by detailing the exact return shape (answer, evidence, confidence, source, fetched_at) and enumerating refusal reason values. It also explains the extra cost and the routing relationship, making the tool self-contained for an agent.

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 schema already covers all parameters with 100% description coverage, listing six aliases for the question field. The description adds no additional parameter semantics beyond what the schema provides, so the baseline score 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 clearly states this is a hallucination-resistant answer mode for high-stakes reads, explicitly distinguishing it from ask_pipeworx by noting it extracts answers only from tool results and returns evidence. It also specifies when to use it (quoted/cited/acted-on answers) and what it returns, making the purpose unambiguous.

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 provides explicit usage guidance: use when an answer will be quoted, cited, or acted on, and when the agent must not invent facts. It also contrasts with ask_pipeworx by noting the extra LLM cost and advising preference for casual lookups, giving clear when-to-use and when-not-to-use conditions.

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