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

Annotations already provide readOnly/openWorld/idempotent safety hints, and the description substantially adds behavioral detail: it returns explicit refusal structures for cases like not_in_source, no_tool_match, and data_truncated, and discloses the extra LLM call cost. It also explains success/failure output contracts, which is critical since there is no output schema.

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?

Dense but every sentence earns its place: purpose, mechanism, output contract, refusal contract, use cases, and cost comparison are all packed in without fluff. Front-loaded with the core value proposition and clearly structured around behavior and usage.

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?

Complete for a high-stakes read tool with no output schema. The description fully specifies return values, refusal reasons, when to use, when not to use, the alternative tool, and the latency/cost tradeoff. An agent has everything needed to invoke it 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%, so the baseline is 3. The description does not add much beyond the schema: it confirms the input is a natural-language question but the schema already documents 'question' and all five aliases. No additional parameter-level semantics are needed or provided.

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?

States a specific, distinctive purpose: a hallucination-resistant, grounded answer mode for high-stakes reads. It directly differentiates itself from sibling ask_pipeworx by explaining the extraction-only-from-tool-result behavior, and the name/title reinforce the distinction.

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 says when to use: whenever answers will be quoted, cited, or acted on and the agent must not invent facts, with concrete examples like financial verdicts and legal claims. It also names the alternative ask_pipeworx and the tradeoff (one extra LLM call), explicitly preferring the sibling for casual lookups.

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