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

Annotations already indicate read-only and non-destructive behavior. The description adds meaningful context: FTC consent orders last 10-20 years, carry $50,000+/day penalties, survive acquisition, and bind the acquirer. It also gives a concrete example and source, plus the $0.50 USDC cost per call, which is useful practical context beyond the 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 the primary use cases, then adds enforcement consequences, a concrete example, source, and pricing. It is longer than the minimal description, but eac sentence adds useful information; nothing feels like filler.

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 read-only, one-parameter tool with no output schema, the description covers the key facts needed for selction and invocation: when to use it, what type of data it surfaces, why it matters, a source, and cost. The main gap is that it does not describe the return format or fields, wich would be more material because no output schema exists.

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 only parameter, company_name, is undocumented in the schema beyond being a string. The description compensates by clarifying that the tool evaluates 'a company' and uses 'Tech Platform Corp' as an example, but it does not specify the expected name format – legal name vs common name, subsidiaries, aliases – or explicitly confirm input requirements. For a single parameter this is adequate but not thorough.

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 clearly names a specific resource – FTC enforcement history – and ties it to concrete use cases: antitrust exposure, consumer protection liability, data privacy enforcement history, and deceptive practices risk. It does not explicitly contrast with sibling enforcement tools like get_occ_enforcement_actions or get_cfpb_complaint_intelligence, but the FTC focus and decision context make the purpose clear.

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?

It provides explicit usage context: before an acquisition, strategic partnership, or enterprise vendor selection, and in specific risk categories. It does not state when not to use it or name alternative tools for adjacent agencies/topics, so some routing responsibility remains on the agent.

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