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pyenv

Manages Python versions via pyenv.

How to control pyenv ↓

What pyenv does on Lint

AI agents invoke pyenv to trigger actions in Lint. 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.

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Why pyenv needs a policy

Managing Python versions via pyenv involves executing pyenv commands that can install, switch, or configure Python environments. This triggers external operations and system changes. The description is brief but pyenv management implies command execution with potential environment-wide effects.

From the tool's definition Manages Python versions via pyenv

Documented attack patterns abuse exactly the kind of access pyenv gives an agent:

How to control pyenv

PolicyLayer is an MCP gateway — it sits between your AI agents and Lint, and nothing reaches the server without passing your rules. This is the rule we recommend for pyenv:

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "pyenv": {
      "limits": [
        {
          "counter": "pyenv_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

pyenv 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.

  1. Create a free account and register Lint — nothing to install.
  2. Add this policy — paste it, or build it visually.
  3. Point your MCP client (Claude, Cursor, anything) at your gateway URL.
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Related tools and policies

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Questions about pyenv

What does the pyenv tool do? +

Manages Python versions via pyenv. It is categorised as a Execute tool in the Lint MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on pyenv? +

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

What risk level is pyenv? +

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

Can I rate-limit pyenv? +

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

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

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

Enforce policy on every Lint tool call.

Start from Lint, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.

Free to start. No card required.

202 Lint tools catalogued and risk-classified — across an index of 43,000+ MCP servers.

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