Install a Python package in the specified environment. Args: package_name: Name of the package to install environment: Name of the Python environment upgrade: Whether to upgrade if already installed timeout: Maximum execution time in seconds
AI agents invoke install_package to trigger actions in MCP Python Interpreter. 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.
Installing a package executes an external process (e.g., pip install) that downloads and runs arbitrary third-party code, modifying the environment's installed packages. This is not a simple write (it runs code/processes and can introduce supply-chain risks), but it is also not purely destructive since packages can be uninstalled.
From the tool's definition Install a Python package in the specified environment — triggers an external pip/package-manager operation that modifies the Python environment by adding or upgrading third-party code
Documented attack patterns abuse exactly the kind of access install_package gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and MCP Python Interpreter, and nothing reaches the server without passing your rules. This is the rule we recommend for install_package:
{
"version": "1",
"default": "deny",
"tools": {
"install_package": {
"limits": [
{
"counter": "install_package_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}install_package 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.
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Install a Python package in the specified environment. Args: package_name: Name of the package to install environment: Name of the Python environment upgrade: Whether to upgrade if already installed timeout: Maximum execution time in seconds. It is categorised as a Execute tool in the MCP Python Interpreter MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the MCP Python Interpreter MCP server in PolicyLayer and add a rule for install_package: 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 MCP Python Interpreter. Nothing to install.
install_package 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 install_package 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 install_package. 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.
install_package is provided by the MCP Python Interpreter MCP server (yzfly/mcp-python-interpreter). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 10 MCP Python Interpreter tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
10 MCP Python Interpreter tools catalogued and risk-classified — across an index of 42,500+ MCP servers.