lerna · Python MCP: Risk & Policy

WHAT IT DOES

What lerna does on Python

AI agents invoke lerna to trigger actions in Python. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.

WHY IT NEEDS A POLICY

Why lerna needs a policy

Lerna's 'run' subcommand executes arbitrary scripts within a monorepo context, making this an Execute tool. The 'version' subcommand modifies package versions and can trigger release workflows. While not inherently destructive or financial, the ability to run arbitrary scripts in a monorepo environment and modify versioning creates high blast radius for misuse.

From the tool's definition Tool description states it 'Runs Lerna monorepo commands' including 'version' which can modify package versions, and 'run' which executes arbitrary scripts defined in package.json.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

lerna Rate-limited

lerna stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "lerna": {
      "limits": [
        {
          "counter": "lerna_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Python, so you can run agents against it safely.

FAQ

Questions about lerna

What does the lerna tool do? +

Runs Lerna monorepo commands (list, run, changed, version) and returns structured package information. It is categorised as a Execute tool in the Python MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on lerna? +

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

What risk level is lerna? +

lerna is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit lerna? +

Yes. Add a rate_limit block to the lerna 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 lerna completely? +

Set action: deny in the PolicyLayer policy for lerna. 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 lerna? +

lerna is provided by the Python MCP server (Dave-London/Pare). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.