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

Annotations already mark the tool as read-only and non-destructive. The description adds meaningful context by detailing the nature of the output (rollups by company/product) and the data source (CFPB Consumer Complaint Database). It goes beyond the annotation baseline, though it doesn't cover pagination or response formats.

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 three information-dense sentences: when to use, what it returns, and a concrete example. Every sentence adds value, and the example makes the purpose immediately understandable. No wasted words.

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 tool's use cases, output content, and provides an example, which suffices for a moderately complex tool with no output schema. It lacks details on filtering behavior beyond product and data limitations, but the example and source mention provide adequate context.

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?

With schema description coverage at 0%, the description must compensate for missing parameter semantics. It mentions 'by company and product' and gives an example using company_name, but does not explain parameter constraints, optionality, or accepted values. This is partial compensation.

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 states the tool returns CFPB complaint rollups by company and product, including volume, issue themes, and response rate trends. It includes a concrete example that illustrates the output. This distinguishes it from sibling benchmark tools by focusing on consumer complaint data.

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 explicitly lists when to use the tool: assessing consumer finance risk, benchmarking complaint volume, or pre-acquisition due diligence. It does not mention when not to use it or name competing alternatives, but the context is clear.

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