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

With no annotations provided, the description carries the full behavioral burden. It usefully discloses case-insensitive matching and a 50-row result cap, but says nothing about return format, ordering, behavior on zero matches, or how rows are represented. Read-only nature is only implied by 'search'.

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. It leads with the resource (rows of the dataset) and folds the matching semantics and cap into the same clause.

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 with no annotations and no output schema, the description conveys what is matched and how many results max come back, but leaves the shape of a returned row unspecified. Adequate, not complete.

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% (only 'query' has a description; 'limit' relies on min/max constraints). The description compensates partially by stating the effective result ceiling ('up to 50'), but the distinct meaning of the limit parameter vs. the return cap is not clarified.

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 names a specific verb (search), a precise resource (rows of the Injection Molding Cost Checker dataset), and a match rule (cells contain the query, case-insensitive). It clearly distinguishes a text-search tool from sibling operations like dataset_row or dataset_top, though it doesn't explicitly contrast itself with them.

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 text) but gives no explicit when-to-use guidance and names no alternatives among the many siblings (dataset_rows, dataset_top, dataset_compare, etc.). The agent must infer that a text search is the right call.

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