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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint), the description discloses rich behavioral detail: the extraction-only-from-results mechanism, the exact success return shape, and the explicit refusal contract with all five refusal_reason enum values ('not_in_source'|'no_tool_match'|'tool_error'|'data_truncated'|'llm_error'). It also reveals the routing scale (5,798 tools across 1,517 sources) and the extra LLM-call cost. Nothing contradicts 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?

The description is dense but every sentence earns its place: purpose, mechanism, success/refusal shapes, use cases, and cost tradeoff. It is front-loaded with the key differentiator. The JSON return/refusal shapes are verbose, but since there is no output schema, they are necessary rather than padding.

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 no output schema, the description fully specifies return values — both the success shape ({answer, evidence, confidence, source, fetched_at}) and the refusal shape with all reasons. Annotations cover the safety profile, the schema covers the parameter, and the description covers usage guidance, cost, and behavioral contract. Nothing an agent needs to call it correctly 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 description coverage is 100% — the schema already documents the single required question parameter with all five aliases (query, q, prompt, text, input). The description adds no parameter-level meaning beyond the schema; its 'fills arguments' phrasing refers to internal sub-tool routing, not the user-facing parameter. Baseline 3 is appropriate since the schema carries the full burden.

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 states a specific verb and resource: 'Hallucination-resistant answer mode for high-stakes reads' that extracts answers grounded in tool results. It explicitly differentiates from the sibling ask_pipeworx ('Same routing as ask_pipeworx... then EXTRACTS the answer using ONLY what the tool result contains'), so an agent can distinguish them without opening either schema.

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?

Provides explicit when-to-use guidance: '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 names the alternative and the exclusion condition: 'prefer ask_pipeworx for casual lookups.' The cost tradeoff (one extra LLM call) gives the agent a concrete decision criterion.

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