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

No annotations are provided, so the description carries the full burden. It discloses case-insensitive matching and the 50-result cap, but it does not state whether the operation is read-only, what the response shape is, or what happens with zero 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, information-dense sentence with the core action front-loaded and modifiers appended. Every word earns its place; there is no 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 simple search tool with no annotations and no output schema, the description covers the search mechanism, scope, and limit. It does not spell out the return format, but 'Rows...' implies row-level results, which is likely enough for a read-only dataset query.

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 describes query ('text to look for in any cell') but not limit. The description adds meaning by specifying case-insensitive behavior for query and 'up to 50' for the limit cap, compensating for the 50% schema coverage.

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 and resource ('Rows of the ReceivableLedger dataset whose cells contain...'), clearly distinguishing it from siblings like dataset_stats or dataset_columns. The only redundancy is the generic title, but the description itself is fully disambiguating.

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 description implies a text-search use case ('cells contain the query') but does not explicitly say when to prefer it over dataset_row or dataset_top. No alternatives or exclusion conditions are named, so the agent must infer the choice from context.

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