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

With no annotations, the description must carry the behavioral burden. It usefully discloses exact matching and case-insensitivity, and 'rows' implies all matches are returned. But it does not mention possible empty results, whether the dataset has unique keys, or any other operational caveats.

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, compact sentence that states the dataset, the selector, and the matching behavior. No filler or redundant content appears.

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 tool, the description covers the core lookup semantics and even adds case-insensitivity. It is less complete regarding the return shape, duplicate matches, and explicit differentiation from sibling tools, though the absence of an output schema makes some of this harder to assess.

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 does clarify that 'column' is the field to match on and 'value' is what to match, plus the exact/case-insensitive rule. However, it provides no additional detail on valid column names, value formatting, or edge cases.

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 title conveys a concrete verb and resource ('Look a row up'), and the description clarifies the matching rule: rows whose column equals a value exactly, case-insensitively. It is specific enough to distinguish from a fuzzy search sibling, though the description's noun-phrase phrasing ('The rows...') is slightly awkward and does not explicitly say 'returns'.

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 implies when to use the tool: when an exact, case-insensitive column match is needed. However, it never explicitly contrasts this with alternatives such as dataset_search or states when not to use it, leaving the agent to infer the intended boundary.

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