EuroComply EU AI Act

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

Annotations are minimal (readOnlyHint=false, openWorldHint=false, destructiveHint=false) and do not explain why a classifier is not read-only; the description resolves this by disclosing that a report is saved and a link returned. It also describes the return payload (tier, compliance score, findings with legal article, required documents), adding real context beyond the annotations.

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?

Two sentences, front-loaded with what the tool does and its outputs, followed by a single actionable instruction; no filler or repetition of schema content.

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 and a 9-required-parameter input, the description covers the missing pieces: it summarizes the returned fields, notes the persisted report side effect, and tells the agent how to handle unknown facts.

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 enum descriptions for all 10 parameters, so the schema carries the semantic burden; the description only says the input is '9 facts about it' without adding meaning to any individual fact. 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?

Names a specific verb (classifies) and resource (AI system), enumerates the four EU AI Act risk tiers it outputs, and is clearly distinct from siblings like estimate_ai_act_fine and list_ai_act_deadlines.

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?

Provides an interaction rule ('ask the user for any fact you do not know instead of guessing'), which is useful, but gives no guidance on when to choose this tool over siblings such as estimate_ai_act_fine or generate_ai_disclosure_notice, nor any prerequisite about what inputs are needed.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds substantive domain context beyond that: it is a maximum (not exact) fine capped by Articles 99/101 and subject to the Art. 99(6) lower-of rule for SMEs, which is real behavioral meaning.

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?

A single dense sentence with no filler, front-loaded with the key concept (maximum administrative fine). Slightly compressed in packing the article references and the SME rule together, but every clause earns its place.

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 does name the returned value (maximum administrative fine) and its legal basis, and covers the three required parameters implicitly. Minor gaps remain (e.g., implied currency, that the result is a static/legal estimate rather than a live query), but nothing blocking.

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%, so baseline is 3, but the description adds meaning to isSme by explaining the Art. 99(6) lower-of rule it triggers, and frames annualTurnoverEur as 'worldwide annual turnover' consistent with the schema. This is a modest but genuine addition over the schema.

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 (Estimate) and resource (maximum EU AI Act fine) with the legal basis (Art. 99/101) and the exact inputs that drive it (violation type, worldwide annual turnover). It is clearly distinguishable from classification, disclosure, or deadline siblings.

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 the calculation context (violation type + turnover, SME lower-of rule) but gives no explicit when-to-use guidance, prerequisites, or exclusions versus the sibling tools. Usage is inferable but not stated.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the agent knows this is a side-effect-free local generation. The description adds that output is 'ready-to-paste' text, which hints at the return format, but says nothing about how notices are structured per channel or language, or about legal caveats.

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?

A single dense sentence with the deliverable front-loaded and no filler. It is efficient, though the language enumeration is slightly redundant with the schema's enum.

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 burden of explaining what the agent gets back, and it only gestures at this with 'ready-to-paste notices'. It does not say whether the result is one notice or one per channel/language combination, nor whether it is structured text or JSON.

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 every parameter including defaults is already documented in the schema. The description echoes the channel set and language list without adding format or behavior detail beyond it, so the baseline 3 applies.

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 (generate) and a specific resource (Article 50 AI disclosure notices), and scopes it precisely to chat, voice and AI-generated content in named languages. This is unmistakably distinct from the sibling tools classify_ai_system, estimate_ai_act_fine and list_ai_act_deadlines.

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 framing 'notices required by Article 50 of the EU AI Act' implies the compliance scenario in which the tool applies, but there is no explicit when-to-use statement, no prerequisites, and no reference to the sibling tools as alternatives. Usage must be inferred from the regulatory citation.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety and closed-world behavior are covered. The description usefully scopes the data to a specific regulation version (Regulation (EU) 2026/1744) and notes the return contents, but adds nothing about result size, ordering, or caching beyond that.

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?

A single front-loaded sentence that packs in scope, source regulation, and returned content with no filler or repetition. Nothing could be removed without losing meaning.

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?

For a read-only, no-required-param listing tool with no output schema, the description adequately conveys the returned fields and the temporal scope. It could be more complete by clarifying the result shape or the semantics of the 'days left' value, but it is sufficient to invoke correctly.

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% and the single optional parameter 'upcomingOnly' is fully documented in the schema, so the baseline of 3 applies. The description mentions 'how many days are left' but does not explain the filter's behavior or default in its own text.

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 names a specific resource (EU AI Act application dates, post-2026 Digital Omnibus) and enumerates the returned fields (what applies, to whom, legal basis, days remaining), so an agent knows exactly what it retrieves. It does not explicitly contrast with siblings like classify_ai_system or estimate_ai_act_fine, but the subject matter is 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?

There is no statement of when to reach for this tool versus the sibling tools, no exclusions, and no prerequisites. The 'every application date' phrasing implies a reference-lookup use case but leaves the agent to infer it.

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