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 states what the tool returns (rows) and the matching behavior, but it does not explicitly mention whether it is read-only, whether it returns all matches or just the first, or how errors are handled. This leaves some behavioral ambiguity.

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 a single, well-structured sentence that conveys the essential information without unnecessary detail. It is efficient and to the point.

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?

For a simple lookup tool, the description is sufficiently complete: it names the dataset, the matching condition, and the result type (rows). It does not specify ordering, limits, or the exact shape of the returned rows, but these are likely secondary for a direct lookup operation.

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 description explains both parameters: 'column' is the field to match and 'value' is the exact value to look for. Since the schema has no parameter descriptions, this coverage is helpful. However, it does not specify allowed column names or value formats, which could be relevant in practice.

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 that the tool returns rows from the TimeCardBook dataset where a column matches a value exactly (case-insensitive). This is a specific verb ('look up') and resource ('TimeCardBook dataset'), and it distinguishes the tool from siblings like dataset_columns (which returns columns) and dataset_stats (which returns statistics).

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 defines the matching condition (exact, case-insensitive) but does not explicitly contrast with alternatives like dataset_search. There is no direct guidance on when to use this tool versus dataset_search or other siblings, leaving the choice to inference.

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