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

With no annotations, the description carries the full burden. It usefully discloses that matching is case-insensitive and capped at 50 rows, but says nothing about result ordering, pagination, permissions, or what happens when cells match partially.

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?

A single front-loaded sentence identifies the dataset, the matching rule, and the result bound with no wasted words.

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 two-parameter search with no output schema and no annotations, the description covers the essentials but omits return shape and ordering. Adequate but with clear gaps for an agent that needs to know what a result row looks like.

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 50% — query is documented in the schema, limit is not. The description compensates by explaining matching semantics (case-insensitive) and the 'up to 50' cap, which conveys limit behavior, so it adds value but does not fully describe the limit parameter's role or default.

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 (search) and resource (rows of the Bank Code Lookup dataset) plus the matching rule and result cap. It is clear what the tool does, though it never names sibling tools like dataset_top or dataset_row, so differentiation from them is left to inference.

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?

There is no explicit guidance on when to use this search versus dataset_top, dataset_row, or dataset_stats. A reader can infer it is for text lookup, but no condition, prerequisite, or alternative is stated.

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