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

The description goes far beyond annotations, detailing refusal behavior with specific refusal_reason values, the exact success response shape, the strict grounding constraint ('using ONLY what the tool result contains'), and the extra LLM call cost. Annotations already signal safety read-only/open-world/idempotent, 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: core purpose, routing mechanism, grounding behavior, exact success/refusal returns, use cases, and cost comparison. The front-loaded first sentence immediately answers 'what is this?' and later sections are compactly structured with dashes and code-like literals.

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?

There is no output schema, so the description correctly compensates by spelling out both the success return shape and the explicit refusal shape with enumerated refusal reasons. Given the tool's moderate complexity, the description covers routing, selection criteria, cost, and safety behavior, making it complete for an agent to select and invoke 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% and the sole required parameter is well documented in the schema ('Your question in natural language. Accepts query, q, prompt, text, input as aliases.'). The tool description itself does not add parameter-level semantics beyond reinforcing that the input is a natural-language question, which is the baseline expected when schema coverage is high.

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 clear, specific purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It then explains the mechanism — routing like ask_pipeworx but extracting the answer using only the tool result — and differentiates itself from its primary sibling by name. 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?

Usage guidance is explicit: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also names the alternative ('prefer ask_pipeworx for casual lookups') and gives a concrete cost trade-off (one extra LLM call). This lets an agent make a confident selection decision without inferring.

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