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

No annotations are provided, so the description must carry the burden. It usefully discloses case-insensitive matching and the 50-row cap, but it does not explicitly state that this is a read-only operation, describe the result shape, or address edge cases like no matches or how rows are ordered.

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 front-loaded sentence with no filler. Every element — dataset scope, matching semantics, case-insensitivity, and cap — earns its place.

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 low-complexity text-search tool, the description covers the essential call-semantics: what is searched, how matching works, and the result ceiling. Without an output schema, it does not specify the exact return fields, but the name and sibling context make 'rows' sufficiently clear.

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% because 'limit' lacks a description. The description adds the case-insensitivity nuance for 'query' and the 'up to 50' cap, but the cap merely restates the schema's maximum. It does not explain default behavior or what happens when more than 50 rows match.

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'), a concrete resource ('Rows of the Upkeepvo dataset'), and the exact matching semantics ('cells contain the query, case-insensitive'). The behavior is distinct from siblings like dataset_row or dataset_stats, so an agent can tell this is a full-cell-match search tool.

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 the use case: find rows containing arbitrary text across any cell. However, it does not explicitly mention when to prefer this over siblings or when not to use it, leaving the agent to infer routing from the tool's name and behavior.

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