Manage ML training jobs for the UIForge sidecar model. Actions: check_readiness (verify training prerequisites), start_training (begin LoRA fine-tuning), get_status (check job progress), cancel_training (stop running job), list_adapters (show available adapters), get_summary (get training statist...
Part of the Ui server.
Free to start. No card required.
AI agents use manage_training to create or modify resources in Ui. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call manage_training repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Ui.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"manage_training": {
"limits": [
{
"counter": "manage_training_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full Ui policy for all 38 tools.
These attack patterns abuse exactly the kind of access manage_training gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Manage ML training jobs for the UIForge sidecar model. Actions: check_readiness (verify training prerequisites), start_training (begin LoRA fine-tuning), get_status (check job progress), cancel_training (stop running job), list_adapters (show available adapters), get_summary (get training statistics).. It is categorised as a Write tool in the Ui MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Ui MCP server in PolicyLayer and add a rule for manage_training: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Ui. Nothing to install.
manage_training is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the manage_training rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for manage_training. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
manage_training is provided by the Ui MCP server (@forgespace/ui-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 38 Ui tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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