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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds meaningful behavioral context beyond that: the data source is IRS Statistics of Income Division, it reflects actual filed returns rather than survey estimates, it includes cost per call, and it gives a concrete example of returned metrics. No contradiction with annotations.

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?

The description is front-loaded with 'Use when' and 'Returns,' then packages use cases, output metrics, an example, source, and pricing efficiently. It is somewhat verbose in phrases like 'from the most authoritative source of actual US business financial performance,' but every major section earns its place.

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 two-parameter read-only tool with no output schema, the description is complete: it states output metrics, gives a concrete example of input and returned values, identifies the authoritative source, and includes cost. The only minor gap, entity_type semantics, is already addressed by the schema's enum and default.

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?

The schema covers industry with examples and entity_type with an enum and default, so the description mainly reinforces what is already present. The 'Healthcare and Social Assistance' example clarifies expected industry values, but the description does not explain the entity_type 'all' option or add substantial parameter-level meaning.

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 opens with explicit use cases and states a specific verb and resource: it 'Returns IRS SOI aggregate statistics from actual filed corporation income tax returns — gross receipts, net income margins, and effective tax rates by industry.' It also distinguishes itself from survey-based sources by noting 'Data reflects actual filed returns, not survey estimates.'

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 gives clear when-to-use context: benchmarking financial performance, M&A valuation comparables, assessing effective tax rates by sector, and financial due diligence. However, it does not name sibling alternatives or explicitly state when not to use this tool, so it stops short of a 5.

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