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

Discloses behavior beyond annotations: returns {answer, evidence, confidence, source, fetched_at, refusal_reason:null} on success, and an explicit refusal with enumerated refusal_reason values on failure. Also reveals that it routes across 5,743 tools and that it uses 'ONLY what the tool result contains' plus the extra LLM call cost. No contradiction with 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?

Packed but efficient: the first sentence establishes identity, the second explains routing and extraction, the third specifies exact return formats, and the final two give usage conditions and cost tradeoff. Every sentence earns its place and critical details 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?

Though there is no output schema, the description fully specifies the success and refusal response shapes, including all refusal_reason variants. Combined with annotations (readOnly, idempotent, non-destructive) and explicit usage boundaries, nothing needed to call the tool 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%, with each alias (q, text, input, query, prompt) documented and question described as 'Your question in natural language.' The description adds no new parameter semantics beyond what the schema already provides, so the high-coverage 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?

States a specific mode: 'Hallucination-resistant answer mode for high-stakes reads.' It contrasts with ask_pipeworx: 'Same routing as ask_pipeworx' and then explains the extraction method, clearly distinguishing from siblings. The verb 'EXTRACTS the answer using ONLY what the tool result contains' makes the purpose unambiguous.

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 provides when-to-use: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' and when-not-to: 'prefer ask_pipeworx for casual lookups.' It names the alternative and even quantifies the 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.