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

With no annotations provided, the description carries the behavioral burden and does well by disclosing case-insensitive matching, cell-level containment, and the 50-row cap. It does not describe ordering, pagination, or exact return shape, but the core behavior is transparent.

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?

The description is a single, front-loaded sentence with no filler. It packs in the operation, resource, matching rule, case sensitivity, and result cap with maximal efficiency.

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 essential behaviors an agent needs: what to search, how matching works, and the maximum result size. Minor gaps around pagination and return format prevent a 5.

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 described, while 'limit' is only hinted at through min/max. The description adds slight context with 'up to 50,' which aligns with the limit parameter, but does not explicitly explain how limit behaves or its default.

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 a specific verb ('search') and resource ('Clipboardo dataset') and clarifies the matching semantics ('cells contain the query'). It clearly conveys what the tool does, though it does not overtly differentiate it from sibling dataset tools.

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 use for finding rows containing arbitrary text, but it does not explicitly say when to use this tool versus sibling tools like dataset_columns or dataset_comparison. There is no when-not-to-use guidance or alternative routing.

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