AI Act Radar

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

With no annotations, the description carries the full burden. It discloses that the tool is informational, not legal advice, and outlines the output contents (articles, guidance updates, templates). However, it does not describe potential side effects, authentication needs, or any limitations beyond the disclaimer, leaving some opacity.

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 concise sentences. The first sentence front-loads the purpose; the second adds output details and a disclaimer. Every sentence earns its place with no wasted words.

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 straightforward lookup tool, the description adequately covers the purpose, key inputs, and what is returned. It lacks some nuance (e.g., what 'recent guidance updates' means or how results are structured), but given the lack of an output schema, it provides a reasonable level of 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?

Schema coverage is 100%, with both parameters having enum values and descriptions. The description adds no parameter-specific detail beyond what the schema already provides, so it meets but does not exceed the baseline.

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 action ('Look up'), a resource ('AI Act obligations'), and the exact scope ('for a specific role and AI-system risk category'). It distinguishes itself from siblings by focusing on role+risk obligations rather than articles or updates alone.

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 gives clear context on when to use the tool (when you need obligations for a role+risk combination) and what it returns. However, it does not explicitly mention alternatives or when not to use it, such as when only a specific article is 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?

No annotations are present, so the description carries the full burden of behavioral disclosure. It reveals the return content ('all related events' and 'official EUR-Lex URL'), but does not explicitly state that the operation is read-only or mention any limitations, edge cases, or safety characteristics. The verb 'look up' implies non-destructive behavior, but that is implicit rather than explicit.

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, well-structured sentence that starts with the verb and resource, includes concrete examples, and contains no redundant or filler content. It earns its place efficiently.

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 simple one-parameter lookup tool with no output schema, the description adequately covers purpose, input format, and return values. It tells the user exactly what will be returned ('all related events' plus the EUR-Lex URL), making it complete enough for this simplicity.

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 covers the single parameter 'article' with 100% description coverage, including examples. The description adds no extra semantic meaning beyond what the schema already provides, 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 uses the specific verb 'look up' and identifies the exact resource (a specific AI Act article) with concrete examples like 'Art. 53(1)(d)' and 'Annex III'. It clearly distinguishes itself from sibling tools by focusing on article-level lookup and returning pipeline events plus the EUR-Lex URL.

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 specific article lookups, but does not explicitly state when to use this tool over alternatives or list exclusions. It gives context by emphasizing 'specific' and examples, but no direct reference to sibling tools or when-not-to-use conditions.

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?

No annotations are provided, so the description carries the burden of behavioral disclosure. It mentions the return type (structured events with specific fields) and optional filters, implying a read-only query tool. However, it does not address pagination, default limits, rate limits, or authorization, which would add valuable transparency.

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 action, and contains no redundant information. Every phrase 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?

For a tool with six optional parameters and no output schema, the description adequately covers the return shape (structured events with titles, summaries, severity, topics, source URLs) and the main filter dimensions. It doesn't explain default behaviors like limit, but those are documented in the schema, so the description is sufficiently complete.

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 83%, so most parameters already have adequate descriptions. The description only restates the filter options ('date range, source, topics, or severity') without adding new meaning beyond what the schema already provides. The baseline of 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?

The description clearly states the tool's purpose: querying EU AI Act regulatory updates from primary sources. It specifies the resource (EU AI Act updates) and the verb (query), and distinguishes it from sibling tools like lookup_article or get_obligation_for_role, which focus on different aspects.

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?

It provides a clear use case: 'Use this when you need recent or historical regulatory changes affecting AI builders, GPAI providers, or deployers.' This gives context but does not explicitly name alternatives or when not to use, lacking full when/when-not/alternative guidance.

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?

With no annotations, the description carries the burden of behavioral disclosure. It usefully states that the result is grouped by topic and severity and returned as Markdown. However, it does not explicitly state that the operation is read-only or non-mutating, though 'Generate a digest' strongly implies it.

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 three short sentences, each serving a distinct purpose: function, use case, and output format. It is front-loaded and contains no filler or redundancy.

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 a simple parameter set (one required, two optional) and the description covers output format and main behavior. Since there is no output schema, the 'Returns Markdown' plus grouping note provides adequate invocation context. A minor gap is the lack of guidance on when to prefer siblings for raw updates, but it is not essential for using this tool.

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 67%, and the description reinforces the 'since' parameter with 'since a given date' and the 'topics' parameter with 'grouped by topic'. The 'language' parameter is self-documenting via enum and default. The description adds some but limited value beyond 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?

The description uses a specific verb ('Generate') and resource ('structured digest of all AI Act regulatory changes since a given date'), and adds grouping by topic/severity and Markdown output. This clearly distinguishes it from siblings like query_ai_act_updates or lookup_article by focusing on digest generation rather than raw queries or article lookup.

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 explicitly states usefulness for weekly compliance reviews or audit prep, which gives clear usage context. It does not mention alternatives or exclusions relative to siblings, but the use-case framing is sufficient for a 4.

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