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

With no annotations, the description carries the full transparency burden. It does disclose an authentication requirement, a response cap, and a lightweight row format, which is meaningful. It does not reveal filter combination semantics (AND/OR), default scope, sort order, or whether score bounds are inclusive, leaving notable behavioral gaps.

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?

Two sentences, front-loaded with the core action and filters, then the limit and auth requirement. There is no filler or repetition of the tool name, and every sentence contributes actionable information.

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?

The description covers the main call surface: filters, scope, result cap, and API key requirement. However, for a tool with no output schema, 'light rows' is vague, and useful details like state code format, default scope, or filter combination behavior are absent. It is adequate for a basic query but not fully self-sufficient.

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 50%, so the description must partially compensate. It maps 'score range' to scoreMin/scoreMax and 'scope us|all' to the scope enum, and it implies the limit through 'up to 100.' But it adds little beyond restating the schema's existing meaning for industries and does not explain states format, leaving those to the agent to infer.

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 uses a specific verb ('Query') and resource ('scored dataset'), and enumerates the filter dimensions (industries, states, score range, scope). It also states the result shape ('up to 100 light rows'). It does not explicitly differentiate from sibling tools like data_records or dataset_sample, so it stops just short of a 5.

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 implies usage: query this dataset when you need filtered company data, with a scope switch between US and worldwide and a 100-row cap. It also gives a prerequisite ('Requires an Engagemii data API key') and clarifies that results are 'light rows.' However, it does not say when to prefer another sibling tool or provide explicit exclusions.

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