AI agents use update_schedule_state to create or update resources in Prometheus MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Prometheus MCP Server environment.
The tool name suggests updating (modifying) the state of a schedule, which is a reversible write operation. Without a detailed description, confidence is moderate. The context of AWS Managed Prometheus suggests this could control monitoring schedules or query execution timing.
From the tool's definition Tool name 'update_schedule_state' indicates modification of schedule state. Description is empty, limiting direct evidence of exact behavior.
Documented attack patterns abuse exactly the kind of access update_schedule_state gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Prometheus MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for update_schedule_state:
{
"version": "1",
"default": "deny",
"tools": {
"update_schedule_state": {
"limits": [
{
"counter": "update_schedule_state_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}update_schedule_state stays usable, but capped — an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
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update_schedule_state. It is categorised as a Write tool in the Prometheus MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Prometheus MCP Server MCP server in PolicyLayer and add a rule for update_schedule_state: 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 Prometheus MCP Server. Nothing to install.
update_schedule_state 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 update_schedule_state 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 update_schedule_state. 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.
update_schedule_state is provided by the Prometheus MCP Server MCP server (awslabs.prometheus-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Prometheus MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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805 Prometheus MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.