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

Beyond annotations (readOnlyHint, idempotentHint), the description richly discloses behavior: it routes to the right tool among 5,798, fills arguments, fetches data, extracts answers only from the tool result, returns a structured success payload with verbatim evidence, and explicitly lists refusal reason enum values. This gives the agent a clear mental model without contradicting 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: mode differentiation, routing behavior, success/refusal return contracts, usage contexts, and cost-aware alternative guidance. It is somewhat long and could be broken into tighter bullet-like sections, but it is well front-loaded and free of filler.

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 the tool's complexity (routing across thousands of tools, multiple refusal modes, and no output schema), the description is complete. It explains the return shape, refusal reasons, success criteria, and when to prefer the sibling tool, so an agent can select and invoke it correctly without additional context.

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%: all six parameters are aliases for the same natural-language `question` field and are documented in the schema. The description adds no parameter-specific semantics, but the schema already fully covers this, so the baseline of 3 is appropriate.

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 ('ask... grounded'), a clear resource (Pipeworx), and a distinctive capability (hallucination-resistant answer mode with evidence extraction). It explicitly differentiates from ask_pipeworx by noting it extracts answers only from tool results and returns refusals when data doesn't answer.

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 when the answer will be quoted, cited, or acted on in high-stakes domains like financial verdicts, legal claims, medical lookups, and public statements. It also names the preferred alternative for casual lookups: 'prefer ask_pipeworx for casual lookups' and notes the cost tradeoff of one extra LLM call.

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