Look up the AI Act obligations for a specific role and AI-system risk category. Returns the relevant articles, recent guidance updates, and applicable templates. This is informational, not legal advice.
| Name | Required | Description | Default |
|---|---|---|---|
| role | Yes | The AI Act role you operate as. | |
| risk_category | Yes | The AI Act risk classification of the system you offer or use. |
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.
Add one secure layer between your agents and this server.
Each tool has a distinct primary purpose: role-based obligations, article lookup, raw updates, and summary digest. Slight overlap exists between query_ai_act_updates and summarize_changes_since, but descriptions clearly differentiate raw events from aggregated summaries, making confusion unlikely.
All tool names follow a consistent verb-first snake_case pattern (get, lookup, query, summarize). The naming is predictable and clear, with no mixed conventions or vague verbs.
Four tools is a well-scoped size for a focused AI Act radar server. Each tool covers a distinct core need without unnecessary redundancy, making the set feel complete and manageable.
The toolset covers key workflows: finding obligations by role, looking up specific articles, querying regulatory updates, and generating change summaries. A minor gap is the lack of direct article text retrieval (only a EUR-Lex URL is provided), but this does not critically hinder common use cases.
