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

No annotations are provided, so the description carries the full disclosure burden, and it delivers little beyond the ordering behavior ('in the order given'). It does not state that the call is a read-only lookup, any permission/scope requirements, error behavior for unmatched values, or anything about the result shape.

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 tightly-sized sentence with no filler, and the core selection logic is front-loaded before the use-case hint. The phrasing is slightly awkward as a noun fragment, but nothing is wasted.

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 read with no output schema and no annotations, the description covers purpose, both parameters, and intended use. It is adequate but missing basic completeness items such as confirmation that it is a read operation and any hint at the returned row shape, given there is no output schema to fall back on.

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 explain both parameters, and it does so reasonably: 'column' is the field matched against, 'values' are the values matched, with result order following input order. It still leaves gaps, notably whether 'column' expects a column name vs. index and what the valid column set is.

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 names a specific operation on a specific resource: retrieving rows of the Bank Code Lookup dataset selected by column/value match. The 'for X vs Y questions' cue gives the agent a clear sense of intent that distinguishes it from generic lookups. It stops short of naming the sibling tools (dataset_search, dataset_top) it competes with, so it does not fully disambiguate within the family.

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 trailing 'for X vs Y questions' clause signals the intended use case, which is genuine context beyond the schema. However, it offers no explicit when-not, prerequisites, or named alternatives among the many dataset_* siblings, leaving the agent to infer boundary cases.

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