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

With no annotations, the description carries the full behavioral burden. It does disclose useful traits beyond the schema: case-insensitive matching and the 50-row cap. However, it omits what happens when more than 50 rows match, result ordering, and whether this is a read-only operation.

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 tight sentence with zero filler. The scoping constraints (cell containment, case-insensitivity, 50-row cap) are all front-loaded and every word contributes to the meaning.

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 2-parameter read tool, the description is nearly complete: it states the result set (rows), the matching rule, and the cap. With no output schema, the return row structure is not described, but this is a minor gap for a straightforward search tool.

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% (query is described, limit is not). The description partially compensates by adding case-insensitive semantics and an implicit 'up to 50' tie to the limit parameter, but it does not explicitly document the limit's meaning or the query matching behavior in full.

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+resource: returning rows of the Venbix dataset, and adds concrete scoping details (cell substring match, case-insensitive, capped at 50). This specificity distinguishes it from sibling tools like dataset_row, dataset_stats, and dataset_columns, though it does not name them 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?

Usage is implied rather than stated: an agent can infer this is the tool to use when searching for text across dataset cells, but there is no explicit guidance on when to prefer it over dataset_row or dataset_top, and no exclusions or alternatives are named.

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