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

With no annotations, the description carries the full burden. It does disclose genuine behavioral traits not in the schema: matching is case-insensitive and applies to any cell, and results are capped at 50. However it says nothing about result ordering or what happens when more than 50 rows match (truncation vs. paging).

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 resource and matching semantics come first and the cap closes it. It is arguably too terse rather than padded, so it does not waste space but also omits useful context.

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 output schema, the description covers matching semantics and the result cap, which is the minimum viable. It leaves gaps around ordering, truncation behavior, and routing versus the other dataset_* tools.

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%: the query param is documented ('text to look for in any cell') while limit is not. The description partially compensates by stating 'up to 50', which explains the effective cap on the otherwise-undocumented limit parameter, but adds no syntax or default detail.

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 concrete verb (search/return) and resource (Rows of the Medicare Plan Comparison dataset) and states the matching rule (cells containing the query). It is specific enough to distinguish from dataset_row or dataset_top, though it never explicitly names those alternatives.

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 when-to-use guidance and no named alternative among the nine sibling tools (dataset_row, dataset_top, dataset_stats, dataset_columns). The text-search semantics imply a use case, but the agent gets no help deciding between this and its siblings.

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