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

Annotations declare readOnly/idempotent/openWorld, and the description complements them by disclosing exact return envelope, explicit refusal reasons, the 'only what the tool result contains' extraction constraint, and the extra LLM-call cost. This adds meaningful behavioral context beyond the annotation hints and does not contradict 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?

Dense but efficient: each sentence covers a distinct facet (identity, mechanics, return shape, usage, cost tradeoff). Despite length, there is no filler and the most decision-relevant facts are front-loaded.

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?

Even without an output schema, the description documents both success and refusal response shapes, enumerates possible refusal reasons, and gives explicit selection criteria. That is sufficient for an agent to invoke the tool and interpret its result 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% — every parameter is documented with aliases. The description adds contextual guidance about what kinds of questions to use, but does not need to add parameter-level semantics because the schema already fully defines them.

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?

Opens with a precise identity — 'Hallucination-resistant answer mode for high-stakes reads' — and immediately distinguishes itself from ask_pipeworx by describing the grounded extraction behavior ('EXTRACTS the answer using ONLY what the tool result contains'). This clearly separates it from siblings like ask_pipeworx and ask_pipeworx_beta.

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 states when to use ('whenever an answer will be quoted, cited, or acted on... must not invent facts') and when not to ('prefer ask_pipeworx for casual lookups'). Names the alternative tool directly and adds the cost tradeoff ('Costs one extra LLM call vs ask_pipeworx').

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