list_dimensions

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

With no annotations, the description carries the full burden. It discloses the output (list of dimensions and metrics) and clarifies that actual dimension values are elsewhere, setting accurate expectations. It doesn't mention read-only nature or pagination, but these are minor for a simple lookup tool.

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?

Two sentences with zero filler. It front-loads the core purpose, adds usage examples, and explicitly redirects to the sibling tool. 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?

For a simple list tool with optional parameters and no output schema, the description covers purpose, use cases, parameter implications through context, and points to the related tool for values. It is complete for its complexity.

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% (both parameters have descriptions), so the baseline is 3. The description adds context on how the tool is used but doesn't supplement the parameter meanings beyond what the schema already provides.

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 lists 69 dimensions and the metrics supporting each, which is specific and actionable. It explicitly distinguishes itself from list_dimension_values by noting that dimension values come from that sibling tool, preventing confusion.

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 provides explicit use cases ('what can I slice this by' and 'which metrics can I break down by Country') and explicitly tells users when not to use it ('Dimension *values* come from list_dimension_values'), giving clear when-to-use and when-not-to-use guidance.

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