Get the seven tracked commercial commitments (AUP, no-train default, output ownership, IP indemnity program, retention default, deprecation notice, safety framework version) for a frontier AI vendor. Each commitment carries the quoted primary-source text, source URL, and effective date.
| Name | Required | Description | Default |
|---|---|---|---|
| vendor_slug | Yes | Vendor slug. One of: anthropic, openai, google-vertex, meta, xai. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure. It explains the return value structure (quoted text, URL, effective date) and the verb 'Get' implies a read-only operation. It does not explicitly state 'no side effects,' but the tone and content are sufficient for a simple retrieval tool.
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 only two sentences. The first sentence states the core purpose and lists all seven commitments; the second explains what each carries. Every word adds value, with no redundant phrasing.
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 one parameter, no output schema, and no annotations, the description is remarkably complete. It enumerates the exact commitments and return fields, so the agent knows what to expect and what inputs are needed. No critical information is missing.
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 schema describes vendor_slug with its allowed values, and the description adds no additional parameter information. Given 100% schema coverage, the baseline of 3 applies; the description neither improves nor worsens parameter clarity.
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 'Get' and identifies the resource as 'the seven tracked commercial commitments' with an explicit list (AUP, no-train default, etc.). This clearly differentiates it from sibling tools like get_framework_version_history and get_partnership_pattern, which target different data.
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 by stating it applies to 'a frontier AI vendor' and specifies exactly what data is retrieved. It does not explicitly name alternatives or exclusions, but the purpose is well-scoped enough for an agent to know when to use it, given the distinct sibling tools.
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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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.