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

Annotations already establish read-only, idempotent, non-destructive behavior, and the description adds substantial behavioral detail: it extracts answers only from tool results, returns verbatim evidence and confidence, and provides an explicit refusal structure with concrete refusal reasons. This goes well beyond the annotation hints and fully discloses failure modes.

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: core behavior, routing model, return shapes, refusal reasons, use cases, and cost trade-off. The most important differentiator ('hallucination-resistant') is front-loaded, and the structure flows logically from behavior to return contract to guidance.

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 fully specifies both success and refusal return structures. It covers the question input, safety profile via annotations, cost trade-off, and sibling relationship to ask_pipeworx. Nothing an agent needs to decide whether to call this tool 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% and the input schema already explains that the question is natural language and lists accepted aliases. The description does not add much parameter-level detail, but it doesn't need to because the schema carries that burden clearly.

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 verb ('answers') and resource (data sourced from Pipeworx tools). It distinguishes itself from ask_pipeworx by emphasizing evidence extraction and refusal behavior, so an agent can tell them apart without opening schemas.

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?

Explicitly states when to use the tool: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also names ask_pipeworx as the alternative for casual lookups and highlights the extra LLM call cost, giving clear selection guidance.

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