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

No annotations are provided, so the description carries the full behavioral burden. It only adds case-insensitive exact matching and omits return format, result limits, sorting, error behavior, authentication needs, and whether multiple rows can match.

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 filler, front-loading the resource and matching rule. It is appropriately sized for a simple lookup tool.

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?

Given no annotations, no output schema, and 0% parameter description coverage, the description is incomplete. It lacks usage context, valid column discovery, result behavior, and error handling, so an agent cannot confidently invoke it beyond the simplest call.

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 description coverage is 0%, so the description must compensate. It identifies the column/value relationship and case-insensitive exact matching, but does not explain valid column names, allowed value types, or how to discover columns, leaving the two required parameters poorly specified.

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 resource (rows of the PropFirmPicker dataset) and precise matching condition (column equals value exactly, case-insensitive). It distinguishes itself from sibling dataset_search by emphasizing exact equality, though it does not name alternatives.

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 explicit when-to-use guidance, alternatives, or exclusions. The exact-match condition implies a lookup scenario, but the agent is not told when to prefer dataset_search for broader queries or dataset_columns to discover valid column names.

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