query_cube_observations

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable context about the limit on observations, label resolution toggle, and the inability to filter server-side, going beyond annotation basics.

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 well-structured with a brief overview, a contextual note about limitations, and an Args list. Every sentence provides essential information without redundancy, making it appropriately concise yet informative.

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 the presence of an output schema and strong annotations, the description fully covers the tool's purpose, limitations, and usage context. It mentions the 'phase 2' pipeline position and provides the needed alternative for heavy queries, making it complete for invocation.

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?

Although the schema lists parameters, the description gives each parameter semantic meaning: cube_uri from search_cubes, language for labels, limit as observation cap, and resolve_labels toggling label replacement. This clarifies how each parameter affects behavior.

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 'Read the actual data points of a cube' with a specific resource and action. It adds details about label resolution and keying by dimension names, distinguishing it from sibling tools like run_sparql.

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 says to use this for reading observations and to use run_sparql for heavy analytical slicing, noting the lack of cheap server-side filtering. This provides clear when-to-use and when-not-to-use guidance with a named alternative.

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