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

Annotations already indicate a harmless read-only operation, and the description adds meaningful context beyond those annotations: it discloses dataset version, record counts, and the categories of metadata returned. This gives the agent a clear idea of what the tool provides without needing to invoke it.

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 compact, bilingual, and information-dense without filler. The core operation is stated first, followed by the supporting dataset details and a one-line use-case sentence, so every sentence 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?

With no parameters, a present output schema, and annotations already covering the safety profile, the description provides all necessary context: what the tool returns, what dataset it describes, and when to invoke it. Nothing critical is missing.

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 tool takes zero parameters, so there is no parameter semantics burden on the description. The baseline of 4 applies, and the description appropriately focuses on what the response contains rather than inputs.

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 ('returns') and precisely identifies the resource (JCCDB metadata, scale, license, download links, citation). It also includes concrete dataset details (95,403 items, v4.0 counts) that make the tool's purpose instantly identifiable relative to the cost-related siblings.

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 says to use this when looking for a primary construction-cost data source, giving clear contextual guidance. It does not explicitly name alternatives or state when not to use this tool, but the use case is specific enough to route an agent correctly.

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