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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds genuinely useful context beyond that: it enumerates the returned fields and gives the underlying endpoint (GET /companies/{companyId}/dataStatus), which compensates for the absence of an output schema. It omits any note on scoping (e.g., all data types vs. one), keeping it from a 5.

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 sentences plus the endpoint reference, front-loaded with the purpose and followed immediately by the use case. No filler, no restatement of the title.

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 correctly names the key returned fields and the endpoint, which is enough for an agent to call and interpret the tool. It does not mention error behavior or pagination, but for a single-company status lookup those gaps are minor.

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% and the single companyId parameter is documented as a UUID in the schema. The description adds no syntax or format detail beyond what the schema provides, so the baseline of 3 applies.

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 verb and resource ('Get the freshness/status of each accounting data type for a company') and names the returned fields (lastSuccessfulSync, currentStatus, dataType), which separates it from the sibling list_* data endpoints. An agent can distinguish it from codat_refresh_data_type and codat_get_data_info without opening the schema.

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 the use case: 'Use this to check whether a dataset has been pulled before reading it.' That gives clear when-to-use context. It stops short of naming alternatives or exclusions (e.g., distinguishing it from codat_refresh_data_type, which mutates the same data), so it falls short of a 5.

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