get_data_value_set

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

No annotations are provided, so the description carries the full burden. It states the tool fetches raw data but does not disclose any side effects, authentication needs, rate limits, or data volume considerations. The description adds basic behavioral context but lacks depth.

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 very concise with no unnecessary words. It fronts the purpose in the first sentence, then lists arguments with clear descriptions. Every sentence adds value.

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 three required string parameters and existence of an output schema, the description covers the tool's purpose, usage context, and parameter details adequately. It lacks information on error conditions or permissions, but for a straightforward fetch tool, this is sufficient.

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%, but the description adds meaningful parameter explanations: 'data_set: dataset UID.', 'period: a single period, e.g. 202401 or 2024Q1.', 'org_unit: org unit UID.' This provides format examples and clarifies the purpose beyond the schema's titles, though it could include sources for UIDs.

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 fetches raw non-aggregated data values from data entry for one dataset, period, and org unit. It explicitly contrasts with analytics tools, differentiating from siblings like get_analytics and get_analytics_raw.

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 explicitly says 'Use when you need the exact captured values rather than analytics output', providing clear context and contrasting with alternatives. It also lists the three required parameters.

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