save_baseline

Save a metric baseline from historical data.

SERVERLogicmonitor SOURCEpypi:lm-mcp

Medium RISK CLASS

Category Write

Parameters 00 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-github-ryanmat-logicmonitor/save-baseline.md

WHAT IT DOES

What save_baseline does on Logicmonitor

AI agents use save_baseline to create or update resources in Logicmonitor, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Logicmonitor environment.

RISK

Why save_baseline is rated Medium

An AI agent can call save_baseline faster than any human can review: one bad instruction and it creates or modifies resources in Logicmonitor by the hundred, each call as confident as the last.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs save_baseline safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Logicmonitor, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For save_baseline, this is the rule to start with:

save_baseline Rate-limited

save_baseline 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "save_baseline": {
      "limits": [
        {
          "counter": "save_baseline_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Logicmonitor, apply this rule, and every save_baseline call is checked against it from then on.

FAQ

Questions about save_baseline

What does the save_baseline tool do? +

Save a metric baseline from historical data. It is categorised as a Write tool in the Logicmonitor MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on save_baseline? +

Register the Logicmonitor MCP server in PolicyLayer and add a rule for save_baseline: 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 Logicmonitor. Nothing to install.

What risk level is save_baseline? +

save_baseline is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit save_baseline? +

Yes. Add a rate_limit block to the save_baseline 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.

How do I block save_baseline completely? +

Set action: deny in the PolicyLayer policy for save_baseline. 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.

What MCP server provides save_baseline? +

save_baseline is provided by the Logicmonitor MCP server (pypi:lm-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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