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

With no annotations, the description carries the full burden. It discloses the case-insensitive matching semantics, which is genuine behavioral context, but says nothing about whether multiple rows can be returned, result limits, pagination, or auth requirements.

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 with no filler, stating the resource and filter condition immediately. It is appropriately sized, though its brevity is partly the source of the definitional gaps elsewhere.

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 tool with no annotations, no output schema, and 0% parameter coverage, the description should do much more. It omits cardinality (one row vs. many), result limits, error behavior for no-match cases, and any column/value format guidance.

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 loosely maps 'column' and 'value' to their roles and adds the case-insensitive matching rule, but gives no guidance on column naming conventions (IDs vs. labels), value formatting, or whether the column name itself is case-sensitive.

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 verb-equivalent (look up rows) and resource (rows of the Stocktaka dataset) with a precise filter predicate (column equals value exactly, case-insensitive). It implicitly distinguishes itself from siblings like dataset_search by specifying exact equality, but never names an alternative tool explicitly.

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?

No when-to-use or when-not-to-use guidance is given; the agent must infer that this is for exact-match lookups rather than search or top-N retrieval. No prerequisites, alternatives, or routing hints are provided despite the presence of six sibling tools.

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