source_caveats

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns caveats and binding status, but does not mention that it is read-only, idempotent, or any error conditions. This is adequate but not comprehensive.

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 two concise sentences. It front-loads the core purpose and immediately provides usage context. No redundant information is present.

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?

Given no annotations, a 5-parameter tool with low schema description coverage, the description is fairly complete: it explains the tool's output and when to use it. The presence of an output schema mitigates the need to document return values. Minor omission: 'params' object not covered.

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 0%, so the description must compensate. It mentions 'date range' and 'unit_classes', hinting at start/end and unit_classes parameters, but does not explain 'dataset_id' or 'params'. Some parameters gain meaning, but significant gaps remain.

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 returns every catalog caveat and whether it binds to the supplied context. It is specific and implicitly distinguishes from sibling tools like 'provenance' or 'find_series' which deal with different aspects of data.

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?

The description gives clear context: it should be used when evaluating history seams and unit_classes for planned combinations of differently scaled series. It does not provide explicit exclusions or alternative tools, but the context is sufficiently clear.

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