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

With no annotations provided, the description properly carries the behavioral burden. It discloses case-insensitive matching, that matching applies to any cell, and that results are capped at 50. Minor gaps remain, such as result ordering and behavior when no cells match, but the key invocation-relevant behavior is stated.

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?

One sentence with no filler; the resource, matching behavior, case sensitivity, and row cap are all front-loaded and useful.

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 tool, the description plus schema is nearly sufficient: the agent knows what to pass, how matching works, and the result limit. It does not describe the output shape or ordering, but no output schema exists and the return concept of 'rows' is implied.

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 only 50% because limit has no description, but the description compensates by explaining the search is case-insensitive and that results are 'up to 50'. This adds meaning beyond the schema for both query and limit.

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 states exactly what the tool returns: rows of the Rapportvo dataset whose cells contain a query, with case-insensitive matching and a cap of 50. This makes the tool's function clear, though it does not explicitly contrast it with sibling tools such as dataset_row or dataset_top.

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 wording establishes a clear use case: search for dataset rows by content across all cells. It does not explicitly name alternatives or exclusions, but the context is sufficient for an agent to prefer this over row/stats/column tools.

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