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

With no annotations, the description carries the full burden. It adds meaningful behavioral details: case-insensitive matching and an upper bound of 50 rows. It does not disclose output shape, ordering, or behavior when no rows match, but the core behavior of a read-only search 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?

A single, front-loaded sentence with no filler. The key qualifiers—case-insensitive and up to 50—are immediately visible, making it easy for an agent to parse quickly.

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?

Adequate for a simple search tool: the description states the main action, target, matching semantics, and result cap. However, with no output schema, no annotations, and no sibling routing, an agent is left to infer output format and how limit behaves, so it is not fully complete.

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 schema documents query but not limit, giving 50% coverage. The description adds value by noting case-insensitive matching for query and the 50-row cap, which overlaps with limit's schema maximum. It does not explain whether limit is optional, its default, or exactly how it applies, so it only partially compensates for the schema gap.

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 Orgplanly dataset where cells contain the query, with case-insensitive matching and a 50-row cap. It clearly distinguishes from siblings like dataset_columns or dataset_stats by framing the result as matching rows.

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 use case is implied: use this when you want rows matching a text query across any cell. However, there is no explicit guidance on when to prefer it over sibling tools like dataset_columns, dataset_row, or dataset_stats, nor any exclusions.

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