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

Annotations already mark it read-only, idempotent, and non-destructive, so the description's additional detail is highly valuable: it discloses refusal behavior, all refusal reason values, the exact return shape, and that it costs an extra LLM call. It even names the tool's conservative extraction policy. This goes well beyond annotation coverage.

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: purpose, routing, extraction behavior, return contract, refusal semantics, use cases, and cost trade-off. It front-loads the distinguishing value and avoids filler. Despite its length, it is efficiently structured.

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?

Given there is no output schema, the description fully compensates by spelling out both success and refusal response shapes, listing refusal reasons, and covering cost and routing trade-offs. An agent has enough to decide, invoke, and interpret results 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 coverage is 100% and the description doesn't add much parameter-specific meaning beyond what the schema already documents. That's acceptable: the one required parameter, question, is self-explanatory and the aliases are fully listed in the schema. The baseline of 3 applies.

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 the mechanism—same routing as ask_pipeworx, then extraction limited to tool result—and names the direct sibling it contrasts with. This leaves 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 gives explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' with concrete examples. It also tells the agent when not to use it: 'prefer ask_pipeworx for casual lookups.' This is exemplary usage routing.

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