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

No annotations are provided, so the description carries the full burden. It usefully discloses case-insensitive matching and a hard cap of 50 rows, but omits result ordering, behavior on zero matches, and whether matching spans all columns or only some. Reasonable but incomplete for a read tool with zero annotation coverage.

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 that front-loads the resource and scope with zero wasted words. Nothing is filler and everything stated is actionable.

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 two-parameter read with no output schema or annotations, the description covers the core matching semantics and result cap, which is the minimum viable. It stops short of describing result ordering or the shape of returned rows, which an agent may need to interpret output.

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, but 'limit' has no description. The description compensates by clarifying 'up to 50' (aligning with the schema maximum) and by adding the case-insensitive, any-cell matching semantics that the schema does not state.

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?

The description gives a precise verb ('Search'), resource ('Procedure Cost Checker dataset'), and scope ('rows whose cells contain the query, case-insensitive, up to 50'). This clearly distinguishes content-matching from siblings like dataset_row (single-row fetch) or dataset_top (ranking), though it never names an alternative 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?

There is no statement of when to use this search versus dataset_top, dataset_row, or dataset_compare, and no prerequisites or exclusions. Usage is only implied by the resource mentioned, leaving the agent to infer routing among nine dataset siblings.

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