Train model
Part of the Scenario Com server.
Free to start. No card required.
AI agents use put-models-train-by-model-id to create or modify resources in Scenario Com. 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 put-models-train-by-model-id 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 Scenario Com.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"put-models-train-by-model-id": {
"limits": [
{
"counter": "put-models-train-by-model-id_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full Scenario Com policy for all 14 tools.
These attack patterns abuse exactly the kind of access put-models-train-by-model-id 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.
Train model. It is categorised as a Write tool in the Scenario Com MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Scenario Com MCP server in PolicyLayer and add a rule for put-models-train-by-model-id: 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 Scenario Com. Nothing to install.
put-models-train-by-model-id 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 put-models-train-by-model-id 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 put-models-train-by-model-id. 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.
put-models-train-by-model-id is provided by the Scenario Com MCP server (scenario.com-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 14 Scenario Com tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.