swarme_ai_modelsList curated AI models available for metered human and machine workflows.
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | ||
| limit | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. 'List' implies a read operation and 'metered' hints at cost implications, but the description doesn't state whether listing incurs charges, requires authentication, or has any default ordering or filtering behavior.
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 with no filler. It is efficiently sized, though phrases like 'metered human and machine workflows' are somewhat vague and could be more informative.
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 operation is simple and an output schema exists, so return structure doesn't need explanation. However, the description is missing when to use the tool, what the sort values mean, and default behavior, leaving clear gaps for an agent choosing or invoking 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 0%, and the description says nothing about the `sort` or `limit` parameters. The enum values ('all', 'hot', 'new') and min/max are self-descriptive at a basic level, but 'hot' is ambiguous and no parameter-level meaning is added in the description.
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 action ('List') and the resource ('curated AI models'), and it adds context by noting they're available for 'metered human and machine workflows.' This distinguishes it from sibling run/status/capability tools at a glance, though it does not explicitly name an alternative.
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 no guidance on when to use this tool versus alternatives like swarme_ai_run, swarme_ai_status, or swarme_capabilities_search. It is easy to infer this is the model catalog, but the description never states that inference or mentions any exclusions.
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.
Tools are grouped into distinct resource families (account, AI, capabilities, discover, experiments, tool runs, uploads, vault), so most are easy to tell apart. However, multiple 'status' tools and the two Vault metadata tools (documents and summary) could be confused without reading the descriptions carefully.
All tools share a consistent swarme_ prefix and snake_case style, making them predictable. The pattern is not uniformly verb_noun, and there is a plural mismatch between swarme_capabilities_search and swarme_capability_describe, but the naming is generally coherent.
22 tools is on the heavy side and above the typical 3-15 range for a focused MCP server. While the platform covers many domains and each tool has a role, several status-related tools could have been consolidated.
Core workflows like tool runs, experiments, AI model jobs, and Vault fills are well covered. Notable gaps remain: no cancellation for AI jobs or experiments, no listing endpoints for runs/experiments, no Vault field read/delete, and the upload session has no companion completion/status tool.