checkout · Python MCP: Risk & Policy

WHAT IT DOES

What checkout does on Python

AI agents invoke checkout 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 checkout needs a policy

Checkout switches git branches or restores working tree files, which is an external operation that modifies repository state (working directory, HEAD pointer). It can cause loss of uncommitted changes and alter the active branch, making it an Execute-level action with high blast radius if misused by an agent.

From the tool's definition Switches branches or restores files... detached HEAD status... new branch was created

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

checkout Rate-limited

checkout 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": {
    "checkout": {
      "limits": [
        {
          "counter": "checkout_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 checkout

What does the checkout tool do? +

Switches branches or restores files. Returns structured data with ref, previous ref, whether a new branch was created, and detached HEAD status. Pass. 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 checkout? +

Register the Python MCP server in PolicyLayer and add a rule for checkout: 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 checkout? +

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

Can I rate-limit checkout? +

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

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

checkout 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.

// THE FULL RECORD

checkout is one line of Python's registry record.

The record carries the whole server: verified identity, auth posture, risk grade, every tool classified, recommended policy — re-checked continuously.

Teams ship this data inside their own products. See what a licence covers →