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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description goes well beyond this by explaining the refusal contract: explicit refusal_reason enum ('not_in_source', 'no_tool_match', 'tool_error', 'data_truncated', 'llm_error'), the success payload shape including verbatim evidence, and the extra LLM call cost. It also states that answers are extracted only from tool results, which is a meaningful behavioral guarantee not present in the 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?

Each sentence earns its place: purpose and grounding mechanism, success/refusal return contract, explicit use cases, and cost tradeoff with a routing recommendation. The most critical information is front-loaded, and the structure is highly scannable despite the density.

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?

For a tool with one meaningful parameter and no output schema, this description is comprehensive: it specifies the exact success response shape, all possible refusal reasons, when to use vs avoid, and the cost implication. Nothing an agent needs to correctly invoke and interpret 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?

The input schema covers all six parameters with 100% coverage, describing the main 'question' field and listing five aliases. The description references the question implicitly but adds no new parameter-level semantics. Baseline 3 is appropriate because the schema already handles parameter meaning completely.

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, distinctive purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It clarifies the verb (answer), resource (Pipeworx data across thousands of sources), and the grounding mechanism ('EXTRACTS the answer using ONLY what the tool result contains'). It is clearly differentiated from the sibling ask_pipeworx by emphasizing evidence and refusal behavior.

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?

Explicit usage criteria are provided: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements).' It also gives an explicit exclusion: 'prefer ask_pipeworx for casual lookups.' This directly contrasts with the sibling tool and leaves no ambiguity about when to choose grounded mode.

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