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

With no annotations, the description carries the burden of behavioral disclosure. It reveals case-insensitive matching, substring containment, and a 50-row cap, but does not mention return structure, ordering, default limit behavior, or empty-result behavior, leaving notable gaps.

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?

The description is a single, focused sentence that front-loads the core behavior and constraints. Every word contributes to the tool's meaning, with no redundant phrasing.

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 covers the essential matching semantics and result cap. However, without an output schema it does not explain the shape of returned rows, the default or optional limit behavior, or ordering, so an agent is left with some uncertainty when invoking it.

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 at 50%: query has a description, limit does not. The description adds the case-insensitive detail for query and indicates an upper bound of 50 results, which partly compensates for the undocumented limit parameter, though it does not state a default or how limit interacts with the cap.

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 clearly states what the tool does: it returns rows of the Hydrantly dataset that contain the query in any cell, case-insensitively, capped at 50 results. This is specific enough to distinguish it from sibling tools like dataset_row or dataset_top, although it does not explicitly name or contrast 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 usage context is implied rather than stated: use this tool when you need to search across cells for a text query. There is no explicit guidance about when not to use it or when a sibling tool would be preferable, so the agent must infer the appropriate choice.

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