Commonality across N moments

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

Annotations already mark readOnly and openWorld; the description adds specifics about deterministic output, verdict values, and survivorship/multiple-comparisons framing. It does not contradict annotations and provides extra behavioral detail.

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?

Two compact sentences that convey purpose, output format, exclusion, and follow-up guidance without fluff. Each clause adds value and the description is well 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?

The description explains the return value composition (per-field verdicts and framing) and disambiguates from prediction tools. There is slight ambiguity around 'framing' but overall it is complete for a read-only tool with no output schema.

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 already fully describes the moments parameter (array of {symbol, at}, min 2, lowercase perp, RFC3339). The description adds no additional parameter-level meaning beyond the schema, so baseline 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 uses the specific verb 'return' and defines the resource as a deterministic intersection with explicit verdict types (agree, partial, disagree, quiet, absent). It also clearly distinguishes from similarity search, differentiating it from sibling tools.

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 'Never similarity search' as a when-not, and provides actionable follow-up advice to test AGREE verdicts with base_rate. This gives clear context for when to use this tool versus alternatives.

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