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

Annotations only declare readOnlyHint=true. The description adds substantial behavioral context: explains the meaning of announced_usd vs announced_govt_only_usd, the exact calculation of reality_ratio with fallback logic, the handling of null denominators, the inclusion of transfers in disbursements, and the existence of reality_ratio_unavailable_reason. This goes far beyond annotations and helps the agent understand edge cases and data limitations.

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 lengthy but well-structured. It opens with the core purpose, then lists returned fields, followed by explanatory paragraphs on semantic nuances. Each sentence contributes to understanding the tool's behavior, with no fluff. While it could be trimmed, the complexity of the data justifies the detail. Front-loaded purpose and field list aid quick scanning.

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 absence of an output schema and the tool's complexity (multiple currency fields, ratio calculations, edge cases), the description is remarkably complete. It documents all return fields, explains calculation logic, notes reporting scopes and limitations, and provides a reason field for missing ratios. An agent can confidently invoke this tool and interpret results without external documentation.

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% with detailed descriptions for all three parameters (country, list_all, program_id). The description adds minor context, such as list_all returning a summary table and program_id accepted formats, but these are largely already present in the schema. For high schema coverage, the baseline of 3 is appropriate; the description does not significantly augment parameter understanding beyond what the schema 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 fetches detailed data on a national sovereign AI compute program, with a specific verb and resource. It distinguishes from siblings by scope (sovereign programs) but does not explicitly name an alternative tool, relying on the tool name and context. The mention of list_all for all programs adds to purpose clarity.

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 does not explicitly state when to use this tool versus alternatives. It mentions that program_id is returned by scrutica_search and list_all, implying a workflow, but gives no direct guidance on when to prefer this tool over search or other get_* tools. No exclusions or alternative routing are provided.

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