pull · Python MCP: Risk & Policy

WHAT IT DOES

What pull does on Python

AI agents invoke pull 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 pull needs a policy

Pulling a Docker image triggers an external network operation that downloads and stores an image locally. This is not a simple read/query—it modifies the local Docker environment by fetching and caching image layers. It qualifies as Execute because it triggers an external operation with side effects (network call, local storage modification), though it is not destructive or financial.

From the tool's definition Pulls a Docker image from a registry and returns structured result with digest info.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

pull Rate-limited

pull 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": {
    "pull": {
      "limits": [
        {
          "counter": "pull_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 pull

What does the pull tool do? +

Pulls a Docker image from a registry and returns structured result with digest info. 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 pull? +

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

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

Can I rate-limit pull? +

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

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

pull 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

pull 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.

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