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

With no annotations, the description carries the full load; it does disclose useful behavior beyond the schema: matching is case-insensitive, it scans all cells, and results are capped at 50. However, it says nothing about ordering, what happens when more than 50 rows match, or the shape of returned rows.

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; the scope (dataset), predicate, and cap all appear immediately.

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 read tool with no annotations and no output schema, this covers the essentials of what is searched, but leaves real gaps: result ordering, tie-breaking when more than 50 rows match, and the returned row structure are unspecified.

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 is documented in the schema). The description adds meaning for query ('case-insensitive', 'any cell') but the limit parameter's effect is only alluded to via 'up to 50', which largely restates the schema's maximum of 50.

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/action (return rows), the resource (the Wärmepumpe Kosten Europa dataset), and the match predicate (cells containing the query, case-insensitive). It is far more specific than the title, but it does not explicitly contrast itself with siblings like dataset_row 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?

It conveys the implied scenario (you have a text fragment to locate) but never states when to choose this over dataset_row, dataset_top, or dataset_compare, nor any when-not condition. No routing guidance is given.

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