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 real behavioral traits: matching is case-insensitive across any cell, and results are capped at 50 rows. It does not state ordering, behavior on zero matches, or any permission/rate considerations, so coverage is partial.

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 the result cap are stated in the first pass. Nothing to trim.

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 or annotations, the description covers matching semantics and result limits. It omits result ordering and return shape, which an agent would benefit from knowing when choosing between this and dataset_top.

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 parameter already documents 'text to look for in any cell', and limit is only bounded by min/max in the schema. The description usefully restates that the match is case-insensitive and that the cap is 'up to 50', but adds little beyond the schema for the query itself.

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 verb (search) and resource (RMMCompare dataset rows) plus the matching semantics: any cell, case-insensitive. Strong and unambiguous, though 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?

Usage is implied by the verb 'search' and the containment semantics, but there is no explicit when-to-use guidance or routing to/away from alternatives such as dataset_row (single row) or dataset_top (ranked rows). An agent can infer intent but must guess at boundaries.

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