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

With no annotations, the description carries the full behavioral burden and does disclose useful traits: matching is case-insensitive and results are capped at 50. It does not state that this is a read-only operation, describe result ordering, or explain what happens with zero matches; the cap is also already implied by the schema's limit maximum.

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 sentence with no waste; the resource is front-loaded and the matching rule and result cap follow in order of usefulness.

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?

No output schema and no annotations, so the description should say more about the return shape (what fields a row contains), any ordering, and whether it is read-only. For a simple search tool this is minimally adequate but leaves real gaps.

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 limit parameter has no description), so the description must compensate, and it does: 'whose cells contain the query (case-insensitive)' explains the matching semantics of query, and 'up to 50' clarifies limit's effect as a result-count cap. It still doesn't explain limit's default or lower bound.

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 concrete operation: return rows of the Stocktaka dataset whose cells contain the query. The matching rule (substring across any cell, case-insensitive) tells an agent what kind of result set to expect. It doesn't explicitly contrast itself with siblings like dataset_row or dataset_columns, so differentiation is only implicit.

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?

No guidance on when to prefer this over dataset_row, dataset_top, dataset_compare, or dataset_columns. Usage as a free-text cell search is inferable, but there are no stated conditions, prerequisites, or exclusions.

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