Generate a structured digest of all AI Act regulatory changes since a given date, grouped by topic and severity. Useful for weekly compliance reviews or audit prep. Returns Markdown.
| Name | Required | Description | Default |
|---|---|---|---|
| since | Yes | ISO 8601 date; the digest covers everything from this date forward. | |
| topics | No | Restrict digest to these topics. Optional. | |
| language | No | en |
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.
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.
