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

Annotations only cover read-only and closed-world, so the description carries real weight and delivers: it explains both timestamp semantics per row, disclosures the receipt hash linkage, states the rate meter ('data.history, US$0, attribution required'), and specifies the empty-result behavior is never metered. That is concrete operational context beyond the 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?

Front-loads what is returned, then clocks, receipts, rate, and empty-case handling in a compact package. It is a dense run-on that could be broken into sentences, but every clause carries distinct information with no filler.

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 must describe the return shape, and it does: rows containing observed_at, fetched_at, and a per-row receipt hash. Combined with rate/attribution and empty-result semantics, an agent has everything needed to call and interpret this tool.

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 coverage is 100% and the single parameter is fully documented with an example format, so the baseline is 3. The description adds little about the parameter itself beyond reinforcing that subject_key must match what the record prints, which the schema already says.

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 scope: 'One subject's change history and current state from oddly's record,' which is clearly distinct from siblings like record_answer (single answer) and record_verify_receipt (verification). The two-clock row model (observed_at vs fetched_at) further pins down the resource being returned.

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?

Usage is implied rather than stated: an agent can infer it should call this to see a subject's history, and the description points to record_verify_receipt for validating row values. But there is no explicit when-to-use vs when-not, and no comparison against record_answer or the other siblings.

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