count_observations

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

No annotations provided, but description adequately describes the tool's behavior (count with optional filters). It doesn't mention return type or side effects, but for a read-only count operation, the transparency is sufficient.

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 efficiently cover purpose and usage guidelines. No redundant or unnecessary words.

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 count tool with no output schema and no annotations, the description is fairly complete. It covers when to use and basic behavior. Lacks explicit return format but is otherwise adequate.

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 0%, and description adds minimal parameter detail beyond 'optionally filtered'. It does not explain cube_uri or the filter structure (dimension, value, operator). The description fails to compensate for the lack of schema descriptions.

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 verb 'Count' and the resource 'observations in a cube', and notes optional filtering. It distinguishes from sibling query_observations by implying it returns a count, not the data.

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?

Explicitly advises using this tool before query_observations to check result size, and suggests actions if count is large (use filters or smaller limit). Provides clear when-to-use and alternatives.

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