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 a 50-row cap (the cap merely restates the schema maximum), but says nothing about read-only nature, permissions, ordering, or what a returned row contains.

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 sentence, front-loaded with the resource and matching rule, no filler. Nothing to trim.

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 two-parameter read tool with no output schema, the description covers the matching contract but omits result shape, ordering, and safety/read-only context. Adequate but with visible 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% (limit is undocumented in the schema). The description adds real semantics for query ('cells contain ... case-insensitive', i.e. any-cell substring match), which the schema does not state, but adds nothing for limit beyond the schema's maximum of 50.

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 clear verb+resource (search rows of the Issafu dataset) plus the matching rule (any cell, case-insensitive) and result cap. It implicitly distinguishes itself from dataset_row, dataset_top and dataset_columns by describing substring matching across cells, though it never names a sibling 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?

There is no guidance on when to use this versus dataset_top, dataset_row, or entities_search. The matching semantics imply a free-text lookup use case, but the agent must infer when this is the right tool.

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