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

With no annotations provided, the description carries the full burden of disclosure. It clearly indicates this is a read-only explanatory tool returning methodology details, with no mention of mutation, side effects, or destructive behavior. The description adequately conveys what the agent should expect: a breakdown of dimensions, weights, and data sources. It reasonably implies a stable, non-destructive operation.

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 a single, information-dense sentence that packs in multiple details: what is returned (sixteen weighted dimensions), the context (Global Metro Power Rankings composite score), and the granularity of information (what each measures, weight/shape, upstream source). It is front-loaded with the subject and purpose. Slightly long but every clause 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?

For a zero-parameter, no-output-schema tool, the description is quite complete. It tells the agent what content to expect (dimensions, weights, data sources) without needing to enumerate every dimension. The complexity is moderate, and the description adequately sets expectations for what the tool delivers. It could theoretically enumerate the sixteen dimensions, but that level of detail is unnecessary for tool selection.

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 has 0 parameters, and schema description coverage is 100%. With no parameters, the baseline is 4 per the rubric. The description adds value by describing what content will be returned, which compensates for the absence of any parameter semantics to clarify.

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 the sixteen weighted dimensions behind the Global Metro Power Rankings composite score. It specifies what each dimension measures, its weight/shape in the formula, and its upstream data source. This is a specific verb+resource and clearly distinguishes itself from sibling tools like get_metro or list_top_metros which deal with metro data, not methodology.

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 implicitly communicates its purpose as an explanatory/reference tool distinct from the data-returning siblings. However, it doesn't explicitly state when a user should call this instead of alternatives (e.g., 'Use this when you need to understand how scores are calculated, not the actual rankings'). The context is clear but lacks explicit when-to-use guidance.

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