This record as markdown: /tools/io-github-dave-london-pare-python/pyenv.md
What pyenv does on Python
AI agents invoke pyenv 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.
| Parameter | Type | Required | Description |
|---|---|---|---|
path | string | — | Working directory |
force | boolean | — | Force reinstallation of an already installed version (--force) |
unset | boolean | — | Clear local/global version setting (--unset) |
action | string | Yes | The pyenv action to perform |
command | string | — | Command name for `which` action (e.g. python, pip) |
compact | boolean | — | Prefer compact output |
version | string | — | Python version string (required for install/uninstall/local/global, e.g. '3.12.0') |
skipExisting | boolean | — | Skip installation if version is already installed (--skip-existing) |
Parameters from the server's own tool schema.
Why pyenv is rated High
Managing Python versions via pyenv involves executing shell commands to install, switch, or configure Python runtimes. This can affect the system's Python environment broadly, and misuse could install arbitrary Python versions or alter the active runtime, impacting all dependent processes. Since it runs external tooling (pyenv) and has system-level effects, Execute is the appropriate category.
From the tool's definition Manages Python versions via pyenv
Risk signalsAccepts file system path (path)
Attacks that exploit this kind of access
The rule that runs pyenv safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Python, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For pyenv, this is the rule to start with:
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.
The button opens the PolicyLayer dashboard: create your workspace, connect Python, apply this rule, and every pyenv call is checked against it from then on.
Questions about pyenv
Manages Python versions via pyenv. 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.
pyenv accepts 8 parameters: path, force, unset, action, command, compact, version, skipExisting. Required: action. The full parameter table on this page comes from the server's own tool schema.
Register the Python 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 Python. Nothing to install.
pyenv is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
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.
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.
pyenv 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.
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