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

With no annotations, the description must shoulder behavioral disclosure. It does add useful behavior: matching is case-insensitive and results are capped at 50 rows. But it omits details such as default behavior when limit is absent, ordering, pagination, or whether any non-read side effects occur.

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 resource and action, then packs the two most decision-relevant behaviors (case-insensitive matching and result cap) with no filler or repetition.

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, the description provides the core contract: matching rows, case-insensitivity, and a 50-row limit. It leaves minor gaps like limit's default value and return shape, but these are manageable given the simple schema and sibling context.

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 only describes query; the description compensates by defining query semantics as searching any cell and adding case-insensitivity. It also reinforces limit's maximum effect ('up to 50'), giving agents a working mental model even though limit has no standalone description.

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 action (return rows matching a query) and a specific resource (the Hreflangly dataset), with key modifiers: case-insensitive and up to 50. It is clear, but it does not explicitly differentiate itself from sibling tools such as 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?

The intended use is implied: call this when you need rows containing arbitrary text. However, there is no explicit guidance about when to prefer dataset_search over dataset_row, dataset_compare, or dataset_top, nor any exclusions or when-not-to-use guidance.

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