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

With no annotations, the description carries the full behavioral burden. It usefully discloses case-insensitive matching, that every cell is searched, and a 50-row cap, but says nothing about result ordering, truncation behavior when there are more matches, or what a returned row looks like.

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?

One tight sentence, correctly ordered: resource, matching semantics, cap.

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 search tool with no annotations and no output schema, the description adequately explains matching and result size but leaves the shape and ordering of returned rows unstated, which an agent must guess before using the results.

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%: query is documented in the schema, while limit has only numeric bounds. The description covers the effective cap ("up to 50") but adds nothing beyond that—no default limit, no ordering effect, no note that limit is optional.

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 and resource: rows of a named dataset matching the query, with matching semantics (case-insensitive, any cell) and a result cap. It implicitly distinguishes itself from dataset_row (id lookup) and dataset_columns, but never names or contrasts a sibling explicitly.

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 description says what the tool returns but never states when to reach for it versus dataset_row, dataset_top, or dataset_compare. There are no prerequisites, no exclusions, and no stated conditions that select this tool over its siblings.

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