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 and a 50-row cap, which are non-obvious and valuable. However, it does not mention return row structure, ordering, read-only behavior, or what happens when no rows match.

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 with no filler. It front-loads the resource and the core action, then adds the two most important behavioral modifiers (case-insensitivity and result cap).

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 search tool with no output schema, the description tells the agent what will be returned (rows of the dataset) and how many. Some detail about the returned rows' structure would strengthen it, but nothing essential for a correct first invocation is missing.

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 documents 'query' as text to look for in any cell, but the description clarifies it is case-insensitive and clarifies the effect of 'limit' by saying results are capped at 50. This compensates well for the 50% schema description 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?

The description specifies the resource (Sacristo dataset), the operation (search rows whose cells contain the query), and the matching behavior (case-insensitive). This clearly distinguishes it from sibling tools like dataset_columns, dataset_stats, or dataset_top.

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 condition 'cells contain the query' makes the intended use clear: find rows by cell content. It does not explicitly name alternatives or exclusion cases, but the context is unambiguous enough for an agent to select this tool when a text search across rows is needed.

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