swarme_ai_statusReturn status and sanitized output for one AI model job.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does convey that the operation returns status and sanitized output, which implies a read-only, non-destructive behavior and discloses that output is sanitized. However, it does not mention error behavior, whether the job runs asynchronously, or what 'sanitized' means in practice.
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. Every word adds meaning, and the key action and object are presented immediately.
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 with only one parameter and an output schema, but the absence of annotations and usage guidance leaves the agent to infer when to call this instead of related status tools. The description is minimally viable but not fully complete for correct tool selection in a large sibling set.
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 does not explain job_id beyond the tool name. While job_id is fairly self-explanatory, the description fails to specify where the ID comes from, its expected format, or any constraints beyond the schema's 'required' flag.
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 a specific action ('Return status and sanitized output') on a specific resource ('one AI model job'). It aligns with the tool name and title, and the phrase 'AI model job' distinguishes it from sibling status tools like swarme_tool_status and swarme_experiment_status, though not explicitly.
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 no guidance on when to use this tool versus alternatives, how to obtain a job_id, or whether to poll it after swarme_ai_run. With many sibling run/status tools, this lack of usage direction is a clear gap.
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.