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

With no annotations, the description carries the behavioral burden. It discloses case-insensitive matching, cell-level containment, and the 50-row cap. It does not describe output order, default limit, or empty-result behavior, but the core search behavior is clearly specified.

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 sentence states the resource, matching rule, case sensitivity, and row cap. No filler or repetition of schema details.

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 two parameters and no output schema, the description plus schema are nearly sufficient. Missing details are minor: default limit behavior and return format.

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 already documents query as 'text to look for in any cell'; the description adds case-insensitivity. The limit parameter is named only through the 'up to 50' phrase, with no description of defaults or interaction with the schema maximum. At 50% schema coverage, the description partially compensates but not fully.

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 clear operation: finding rows whose cells contain the query, and adds useful semantics (case-insensitive, max 50). It does not explicitly contrast with siblings like dataset_row or dataset_stats, so it stops short of full sibling differentiation.

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?

Usage is implied: use this tool when you need to search dataset rows by text content. No explicit when-to-use or when-not-to-use guidance is given, and no alternatives are named, so an agent must infer the decision from the tool names and description.

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