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

No annotations, so the description carries the full burden. It does disclose useful traits: matching is case-insensitive, the scan covers any cell, and results are capped at 50 (implying truncation), but it says nothing about permissions, ordering, or what happens when more than 50 rows match.

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 tight sentence with zero waste, front-loading the resource and scan scope before the matching rule and cap.

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?

There is no output schema and no annotations, so the description is the only source of behavioral detail. It covers what is returned (rows) and the cap, but omits result ordering, pagination/truncation signaling, and any access requirements, leaving meaningful gaps for a 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% (only 'query' is described), so the description must compensate. It adds real meaning the schema lacks: matching is case-insensitive and applies to any cell, and 'up to 50' explains the limit parameter's ceiling, though acceptable values for limit are left to the schema.

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?

States a specific verb (search) and resource (rows of the Lettza dataset) plus the matching mechanism (cells containing the query). It is clearly distinguishable from siblings like dataset_row or dataset_stats, though it never names an alternative to disambiguate further.

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?

No when-to-use guidance and no exclusions. With six siblings (dataset_row, dataset_top, dataset_compare, etc.) the agent gets no help deciding when a free-text cell search is preferable to a row fetch or a top-N query.

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