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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It describes what the tool does but does not mention whether it is a read-only operation, possible side effects, failure modes, or requirements. This leaves significant behavioral context unstated.

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, front-loaded sentence that efficiently states the verb, resource, scope, and output. Every word earns its place with no unnecessary elaboration.

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 is simple with one optional parameter. The description explains the output format (per-token breakdown + overall score), which compensates for the lack of an output schema. However, it does not cover edge cases such as missing entities or the interpretation of the 'Ampel' score, leaving moderate gaps in contextual completeness.

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?

The input schema fully describes the single optional parameter entity_id, providing 100% coverage. The description adds no additional detail beyond what the schema already states, so the 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 aggregates the MiCA Ampel for an entity across all custodied tokens, distinguishing it from sibling tools like assess_entity and assess_token. It also specifies the output includes a per-token breakdown and overall score, making the purpose specific and unambiguous.

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 implies usage for an entity-wide aggregate view, but does not explicitly state when to use this tool versus alternatives or provide exclusions. The phrase 'ALL tokens' contrasts with per-token tools, but without explicit naming or differentiation, guidance remains only implicit.

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