query_observations

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

No annotations are provided, but the description discloses the need to learn cube structure beforehand and the use of dimension/measure paths. It could mention output format or side effects, but queries are inherently safe.

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 concise (3 sentences) with key instructions front-loaded. Every sentence is informative, no redundancy.

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?

Given no output schema, the description could explain the return format. However, it sufficiently covers usage and prerequisites, making it largely complete for a query tool.

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 only 29% (measures and dimensions have descriptions), but the description adds crucial context: dimensions and measures should be paths from get_cube_structure, and filters should use values from get_dimension_values.

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 it queries observations from a data cube with optional filtering and pagination. It distinguishes from siblings by referencing related tools like get_cube_structure and get_dimension_values.

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 explicitly says 'ALWAYS call get_cube_structure first' and instructs to use get_dimension_values for valid filter values, providing clear when-to-use and alternative tools.

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