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

Annotations already declare readOnly and idempotent, but the description goes far beyond: it details the success return shape (answer, evidence, confidence, source, fetched_at, refusal_reason), the refusal structure with specific refusal_reason enum values, and the behavior of using 'ONLY what the tool result contains'. This is rich behavioral disclosure.

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?

Despite being long, every sentence earns its place: purpose, mechanism, output format, refusal modes, usage criteria, and cost comparison to the sibling. Key facts are front-loaded ('Hallucination-resistant answer mode') and the structure flows logically from purpose to behavior to guidance.

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?

With no output schema, the description fully explains the return contract including success and refusal variants, and lists all refusal_reason values. It covers cost, when to use, and how it routes, so an agent has everything needed to select and invoke the tool 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?

The input schema has 100% coverage, documenting the 'question' parameter and its five aliases. The description does not add parameter-level semantics beyond that, which is acceptable because the schema fully handles parameter meaning.

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 purpose: 'Hallucination-resistant answer mode for high-stakes reads', clearly distinguishing it from ask_pipeworx by adding extraction of the answer only from the tool result. It names the sibling tool 'ask_pipeworx' and describes the routing relationship, so an agent can tell them apart without opening schemas.

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 this tool: 'whenever an answer 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 tells when not to use it: 'prefer ask_pipeworx for casual lookups', and notes the extra LLM call cost as a decisive factor.

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