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

With no annotations provided, the description carries the full burden, and it does disclose useful behavior: matching is case-insensitive, it scans any cell, and results are capped at 50. However, it says nothing about ordering of results, pagination beyond the cap, or whether the operation is read-only and side-effect free, leaving real gaps for a dataset query tool.

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 that front-loads the scope (which dataset), then the match semantics, then the result bound. No filler, no redundancy, everything 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 two-parameter search tool with no output schema and no annotations, the description covers the essentials: dataset scope, match rule, case sensitivity, and result cap. It could go further by describing the shape of a returned row or the ordering, but nothing critical to correct 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?

Schema coverage is only 50%: the query parameter is documented in the schema, but limit has no schema description. The description compensates by stating the cap ('up to 50'), which explains the limit's effective max, and by clarifying that query matches any cell case-insensitively. That is meaningful added value beyond the structured fields.

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) and resource (rows of the Historia Pojazdu VIN dataset) and defines the matching rule (cells containing the query, case-insensitive). It is clear on its own, but it does not explicitly position itself against siblings like dataset_row, dataset_top, or dataset_compare, so an agent must infer the distinction from semantics alone.

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 statement of when to use this tool versus the nine siblings (e.g., dataset_top for ranked results, dataset_row for a single record, dataset_compare for diffing). Usage is only implied by the substring-match wording; there are no exclusions, prerequisites, or alternative routing guidance.

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