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

With no annotations provided, the description carries the behavioral disclosure burden. It discloses case-insensitive matching, that any cell can be searched, and a hard cap of 50 rows. Minor omissions include ordering and empty-result behavior, but the core behavior is clear.

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 efficient sentence that front-loads the key behavior and constraint. Every word earns its place, with no redundant filler.

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, low-complexity search tool with two parameters and no output schema, the description is close to complete: it tells the agent what to search, how matching works, and the result cap. It is not exhaustive on return shaping, but 'rows' is explicit and the sibling context makes the tool's role clear.

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 description coverage is only 50%: query is documented, limit is not. The description adds meaning by clarifying case-insensitive matching across cells and by indicating the 50-row cap, which maps to the limit parameter and adds value beyond the schema. It stops short of explaining limit's default behavior when omitted.

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?

States a specific resource (Scopedly dataset), a clear search action over cell contents, and a concrete scope (case-insensitive substring matches, up to 50 rows). This distinguishes it from siblings like dataset_columns or dataset_stats, which have different purposes.

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?

Usage is implied by the search semantics: use when you need rows whose cells contain some text. However, it does not explicitly differentiate from dataset_row (single-row lookup) or dataset_compare, nor does it state when not to use it.

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