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

With no annotations the description carries the full burden, and it does disclose real behavior: case-insensitive matching, matching across any cell, and a 50-row cap. It omits the return shape, ordering, and whether results are paginated, which matters for a search tool with no output schema.

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 matching semantics and result cap are packed in without redundancy.

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 documented two-parameter search tool with no annotations and no output schema, the essentials for invoking (what query matches, result cap) are present, but return structure and ordering are left entirely implicit.

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 no description there. The description's 'up to 50' compensates by implying the cap on results, but it doesn't state the default or that limit controls the row count. Baseline 3 fits.

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) against a specific resource (rows of the Laser Materials Compare dataset) and clarifies the matching semantics ('whose cells contain the query'). It does not, however, distinguish itself from siblings like dataset_row or dataset_top, which also surface rows.

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?

There is no when-to-use guidance and no named alternative. An agent must infer for itself that this is the full-text path versus dataset_row (by key) or dataset_top (by ranking). No exclusions or prerequisites are given.

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