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

Annotations already declare readOnly/idempotent/non-destructive, and the description supplements them with what is returned (risk class, article-numbered obligations, application date, fine ceiling) and the specific trap areas (Annex III list, Article 6(3) exemption, staggered dates) where models commonly err. It omits any caveats about input completeness or determinism, so a 4 rather than 5.

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?

Front-loaded with the user-facing questions before the formal definition, so an agent can pattern-match quickly. The enumerated question list is long but each item maps to a distinct use case; no sentence is pure filler.

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?

With no output schema, the description carries the return-value burden and does so explicitly (class, obligations, dates, fine). For a 10-parameter, zero-required classifier it does not address the legacy/pre-cutoff pathway despite placed_on_market_before_cutoff being an input, leaving one meaningful gap.

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 70% and the schema enum lists are already exhaustive for annex_iii_use_case and prohibited_practice. The description loosely maps concepts to parameters (Article 5 to prohibited_practice, Article 53 to general_purpose_model, Article 50 to transparency_obligation_article_50), which is helpful, but it never explains does_profiling, placed_on_market_before_cutoff, or how role changes the answer.

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?

States a specific verb and resource ('Classifies an AI system under Regulation (EU) 2024/1689') and enumerates the concrete outputs (risk class, obligation set with article numbers, application date, maximum fine). It also names the legal domains covered (Article 5, 6, Annex III, 53, 50), which cleanly separates it from the unrelated siblings cbam_cost and uae_corporate_tax.

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 final sentence gives an explicit alternative: 'Use this rather than recalling the classification,' and lists example questions that trigger use. It gives no conditions under which NOT to use the tool and no prerequisites, but the retrieval context is clear.

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