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

Discloses behaviors beyond annotations: refusal reasons (not_in_source, no_tool_match, etc.), the extra LLM call cost, and the promise of verbatim evidence. This complements the readOnly and idempotent annotations without contradiction.

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 well-structured with a clear flow, but includes some specific details (e.g., 'from 5,907 across 1540 sources') that are not essential for invocation. It remains mostly concise and readable.

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?

Provides complete context: the return shape, refusal conditions, use cases, cost implications, and comparison to sibling tools. Given the parameter complexity is low (aliases) and no output schema, the description fully equips an agent to call it 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 schema covers all 6 parameters with 100% description coverage, each alias clearly linked to 'question'. The tool description adds no additional parameter nuance, so this is at the baseline for high schema coverage.

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 clearly states the tool's purpose: a hallucination-resistant answer mode for high-stakes reads, distinct from ask_pipeworx by extracting answers only from tool results. It also specifies the return format, making the function's role 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 tells when to use it ('whenever an answer will be quoted, cited, or acted on') and when to prefer the sibling tool ('prefer ask_pipeworx for casual lookups'), providing clear routing guidance.

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