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

With no annotations provided, the description carries the full burden, and it does disclose two real behaviors: matching is case-insensitive and results are capped at 50 rows. It omits other traits an agent would want, such as result ordering, pagination beyond the cap, and error behavior on no matches.

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 that front-loads the resource ('Rows of the Bags That Pay dataset') and folds in the match semantics and result cap with zero filler.

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 tool with no annotations, no output schema, and only 50% parameter coverage, the description covers the essentials (what matches, how many rows) but says nothing about what a returned row looks like or how the 50-row truncation should be interpreted. Adequate but with clear gaps.

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 50% (query is documented, limit is not). The description adds meaning for both: it clarifies query matches against 'cells' case-insensitively and confirms the 50-row cap that limit's maximum already encodes, but adds no syntax or format detail beyond that.

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 gives a specific verb+resource: fetching rows of the 'Bags That Pay' dataset that match a query, with an explicit cap of 50. An agent can tell it apart from dataset_top or dataset_row by the text-matching semantics, though no sibling is named explicitly to contrast against entities_search or dataset_compare.

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?

There is no when-to-use guidance and no mention of alternatives such as entities_search or dataset_top. The usage context (a text query against cells) is only implied by the wording, leaving the agent to infer when this tool is the right choice.

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