get_gdpr_statistics

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

No annotations provided, so the description must convey behavioral traits. It indicates read-only aggregate statistics but does not mention idempotency, latency, or authorization requirements beyond the implied API key. The description does not clarify that the api_key parameter is required (the schema shows 0 required parameters, creating ambiguity).

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 a single sentence that lists key statistics, which is efficient and front-loaded. No unnecessary words, but it could be slightly more structured for readability.

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 partially compensates by listing example metrics but does not specify the return format (e.g., JSON structure). It also does not explain how the api_key parameter is used. This leaves some ambiguity for an AI agent.

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 one parameter (api_key) described in the schema. The description does not add any additional meaning beyond the schema's parameter description, so a baseline score of 3 is appropriate.

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's purpose: 'Get aggregate GDPR enforcement statistics' and lists specific metrics (total decisions, total fines, average fine, etc.). It distinguishes from siblings like get_decision_detail by focusing on aggregate 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?

The description says 'Useful for contextualizing advice with enforcement trends,' which implies when to use it. However, it does not explicitly mention when not to use it or contrast with siblings, though sibling names provide some context.

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