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

Annotations already cover readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is established. The description adds metric and geography scope but does not disclose operational behavior such as return shape, aggregation, wildcard effects, or lookup defaults. No contradiction exists.

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 two sentences with front-loaded, specific metric content followed by a short use-case statement. It contains no filler, no repetition of schema details, and 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?

The schema fully documents all conditional geography parameters and the annotations cover read-only/idempotent behavior, so most invocation requirements are already structured. The description adds the output metrics and use cases, which is valuable because there is no output schema. A small note on how to choose among geography levels would improve it further.

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?

Schema description coverage is 100%, and each parameter already has a meaningful description including conditional requirements and wildcard usage. The description adds no parameter-specific semantics beyond the general geography scope, so the baseline score of 3 is appropriate.

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 names a specific data domain—household income, per capita income, housing units, occupancy, home value, and rent—for US geographies. This concrete metric list makes the tool's purpose clear and distinguishes it from sibling census tools like census_demographics or census_population without opening the schema.

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 explicit use cases ('real estate AI, market analysis, location-based pricing'), which provides useful context. However, it does not compare against sibling census or real-estate tools, nor does it say when not to use this tool, leaving alternative selection to inference.

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