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

With no annotations, the description must carry the full behavioral burden. It discloses exact and case-insensitive matching and that multiple rows may be returned, but it does not explicitly state read-only behavior, return format, empty-result handling, or error behavior.

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 efficient sentence with no filler. The core matching behavior is front-loaded, and the case-insensitive qualifier adds valuable information without redundancy.

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?

The tool is simple with only two string parameters, and the description conveys the essential lookup semantics. However, the singular title 'a row' conflicts with the plural description 'rows', and there is no output schema or annotation to clarify return shape, multiplicity, or safety.

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 input schema has 0% description coverage, but the description gives both parameters meaning: 'column' is the field to match and 'value' is the target value. It does not explain allowed column names, formatting, or how case-insensitivity applies to the value beyond the match behavior.

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 defines the tool's result: rows from the Fitouto dataset where a specified column exactly equals a provided value, with case-insensitivity. This distinguishes it from sibling search tools, though the description is phrased as a noun phrase rather than an explicit verb+resource statement.

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 exact-match and case-insensitive semantics imply when to use this tool, but it does not explicitly state when to prefer it over dataset_search or other siblings. There are no exclusions or alternative naming, leaving some inference to the agent.

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