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

With no annotations, the description carries the full burden, and it delivers: score scale (0-1), meaning of 1.0, null behavior for unscored layers, machine-readable placeholder flagging via no_coverage, instruction to relay caveats verbatim, and concrete coverage boundaries. This is unusually transparent.

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 dense and information-rich, front-loading the tool's purpose before coverage details and input requirements. It is longer than minimal but each clause adds needed context; a slight restructuring could improve skimmability without losing value.

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 multi-layer scoring tool with no annotations and no output schema, the description covers purpose, score semantics, null handling, coverage constraints, parameter relationships, and data sources. An agent has what it needs to invoke the tool correctly and interpret results sensibly.

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 already describes each parameter at 100% coverage, but the description adds critical relational semantics: the parameters are alternatives, not complements, and addresses go through the US Census geocoder. This meaningfully prevents incorrect calls where both lat/lon and address are supplied.

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 identifies the tool as providing due-diligence layer scores for US locations and enumerates the specific layers covered. It is concrete and well-scoped, but it does not explicitly contrast with siblings like environment_near or drinking_water, so some inference is required to distinguish them.

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 gives explicit input alternatives ('Pass EITHER a street address... OR lat/lon'), the geocoding source, and detailed geographic coverage limitations. It does not explicitly state when to prefer a sibling tool, but the coverage notes effectively tell the agent where this tool applies versus where it may not.

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