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

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=true. The description adds useful behavioral context beyond annotations by specifying the data sources (EUR-Lex, OECD) and the nature of the return (warnings, citations, recommendations). It does not detail any side effects, but for a read-only simulator this is sufficient.

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 two sentences, front-loaded with the core purpose, and every sentence adds value. It avoids redundancy with the schema and annotations.

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?

The tool has an output schema (per context signals), so return values are already structured. The description fully covers the tool's purpose, inputs, outputs (feedback type), sources, and target audience. Nothing essential is missing for a simulation tool with readOnly and idempotent annotations.

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 description coverage is 100%, so the schema already documents all six parameters with clear descriptions. The description broadly mentions 'AI system descriptions, intended use cases, and technical specifications' which maps to the main parameters but adds no syntax or format details beyond what the schema provides. Baseline 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 a specific verb ('simulating') and resource ('EU AI Act regulatory sandbox submissions'), and distinguishes itself from sibling tools like ai_act_incident_response and ai_act_training_data_audit by focusing on sandbox submissions. It also names the deliverable (structured feedback on compliance, risk levels, documentation).

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 provides clear context on when to use it—for simulating regulatory sandbox submissions and obtaining compliance feedback for legal teams and AI developers. However, it does not explicitly state when not to use it or name alternatives, which would warrant a 5.

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