Current AI legislation headlines across the US Congress, UK Parliament, and EU, with their legislative stage (proposed / committee / floor action / passed / implementation).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max headlines (default 50, cap 100). | |
| jurisdiction | No | Optional filter to one jurisdiction. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, which is not contradicted. The description adds useful context beyond the annotation: it clarifies the geographic coverage and the concept of legislative stage, giving the agent a better sense of the tool's behavior without repeating the safety cue.
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 one sentence, front-loaded with the key term 'Current AI legislation headlines', and includes the legislative stage enumeration. Every word contributes value with no 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?
For a simple read-only tool with two optional parameters and no output schema, the description sufficiently conveys what is returned (headlines with legislative stage) and the scope. It is complete enough for an agent to select and invoke the tool 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%, with each parameter (limit, jurisdiction) well-documented. The description does not add significant parameter semantics beyond what the schema already provides, so the baseline score 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: returning current AI legislation headlines across US, UK, and EU, with legislative stage. It specifies the resource (AI legislation) and scope (three jurisdictions), distinguishing it from sibling tools like get_china_signal or get_topic_signal.
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 provides clear context on when to use the tool (when AI regulation headlines are needed) and implicitly defines scope. It does not explicitly contrast with alternatives or list exclusions, but the jurisdiction list and legislative stage terminology make the intended use obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools are clearly scoped to a specific query type (region, topic, recent items, etc.). The only overlap is get_china_signal as a preset of get_region_signal, but the description explicitly labels it as a shortcut, reducing ambiguity.
All tools use verb_noun with underscores, but the mix of get_, list_, and search_ verbs and the get_china_signal preset (a value rather than a dimension) make it slightly less uniform than a pure get_* set.
10 tools is within the ideal 3-15 range and covers the core operations of an AI news intelligence service without bloat.
The set covers regional/topic signals, recent updates, daily synthesis, regulation, blind spots, search, and related-item exploration. Minor gaps: no tool to fetch full article bodies (paywalled) and no explicit topic list, but core workflows are functional.