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

Annotations already declare readOnly, non-destructive, idempotent, and open-world behavior. The description supplements this with exact data scope (621 cities, 2020 measurement year) and the specific output fields (open space percentage, street space, combined share, population count with access). No contradiction with 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?

Four sentences, each earning its place: what it does, coverage, output fields, filters, and use cases. The primary action is front-loaded, and there is no redundant wording or 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?

The description covers the metric, scope, return values, and available filters. An output schema exists for detailed return structure, so the description does not need to explain that further. It is complete for an agent to select and invoke the tool correctly.

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 coverage is 100%, and each parameter has a detailed description (partial match, case-insensitive, default/max limit). The description only repeats "Filter by country, city, or UN region," adding no meaning beyond the schema, so the baseline score of 3 applies.

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 opens with a precise verb and resource: "Retrieve the share of urban built-up area allocated to open public spaces and streets" and names the SDG indicator 11.7.1. It clearly distinguishes itself from sibling UN-Habitat urban metrics like land_consumption and transport_access by specifying exactly which metric it computes, along with coverage details (621 cities, 2020).

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 states the filters (country, city, UN region) and explicitly suggests relevant use cases: green space equity, climate resilience, and liveability analysis. This gives clear context for when to use the tool, though it does not explicitly name sibling alternatives or exclusion criteria.

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