Get the full version ledger of a frontier AI lab's safety framework (Responsible Scaling Policy, Preparedness Framework, Frontier Safety Framework, or equivalent). Returns every published version, effective date, published changelog, and primary source URL.
| Name | Required | Description | Default |
|---|---|---|---|
| lab_slug | Yes | Lab slug. One of: anthropic, openai, google-deepmind, meta, xai. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It states what is returned, but does not explicitly confirm read-only nature, potential rate limits, or pagination behavior. For a simple retrieval tool, this is acceptable but not fully transparent.
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 main action and quickly elaborating on return contents. Every word earns its place 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 one-parameter list-retrieval tool with no output schema, the description covers the essential information: what the tool does, what it returns, and how to specify the lab. It lacks only minor details like response format or error handling, but these are not critical 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 fully describes the only parameter (lab_slug) including allowed values, meeting the 100% coverage baseline. The description adds no further parameter detail, 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 a specific verb ('Get') and resource ('full version ledger of a frontier AI lab's safety framework'), clearly differentiating from sibling tools focused on partnerships and vendor commitments. It also lists example frameworks and the output fields, leaving no ambiguity.
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 context of when to use the tool is clear: whenever you need version history of a lab's safety framework. However, it doesn't explicitly mention when not to use it or reference alternatives, though the sibling tools are distinct enough that no confusion is likely.
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 targets a distinct entity: framework versions, partnership patterns, partnerships by pattern, vendor commitments, and regulatory provisions. No overlap in purpose.
All tool names follow a consistent verb_noun pattern using snake_case (get_*, search_*). Predictable and uniform.
5 tools is well-scoped for the domain of AI governance tracking. Each tool covers a distinct aspect without bloat.
The set covers frameworks, partnerships, vendor commitments, and regulatory search. A minor gap is the absence of a tool to retrieve details of a single partnership by name, but core workflows are supported.