List every tracked public AI strategic partnership that Anteroom has classified as an instance of a given pattern. Returns partnership metadata with permalink to the primary-source-anchored deal page.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max number of partnerships to return. Default 20, max 50. | |
| pattern_slug | Yes | Pattern slug. See get_partnership_pattern for the list. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full transparency burden. It discloses that this is a listing operation returning metadata and a permalink to a primary-source-anchored deal page, which is meaningful context. It does not explicitly state read-only behavior, but 'List' implies no mutation; the lack of side effects is consistent.
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 a single, front-loaded sentence that clearly states the action, scope, and return value. No wasted words or redundancy—it is a model of conciseness.
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?
Given the simple nature (2 params, no output schema, no annotations), the description is complete: it explains what is returned, the filtering criterion, and the source integrity. The schema covers parameter details, so the description fills the remaining gaps effectively.
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 already described in the input schema. The tool description adds no additional parameter semantics beyond what the schema provides, matching the baseline of 3.
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 'List' and clearly identifies the resource: 'every tracked public AI strategic partnership' filtered by a pattern. This distinguishes it from siblings like get_partnership_pattern, which likely returns patterns themselves, and makes the purpose unmistakable.
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 implies when to use it (when you need partnerships matching a pattern) but does not explicitly state alternatives or exclusions. The schema mentions get_partnership_pattern for finding pattern slugs, but this is not in the description text, so guidance is limited to inference.
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.