Execute Python code in a sandboxed subprocess and return stdout/stderr
AI agents invoke code_execute to trigger actions in A-Modular-Kingdom. 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.
Executing Python code in a subprocess, even if sandboxed, allows an AI agent to run commands whose side effects are unpredictable and depend entirely on the code string passed as input. This is not a simple read (no data retrieval), write (no reversible data modification), or destructive operation (though damage is possible). The primary capability is code execution.
From the tool's definition Tool name 'code_execute' and description states it 'Execute[s] Python code in a sandboxed subprocess and return[s] stdout/stderr'—this directly runs arbitrary code whose effects depend on the arguments provided.
Documented attack patterns abuse exactly the kind of access code_execute gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and A-Modular-Kingdom, and nothing reaches the server without passing your rules. This is the rule we recommend for code_execute:
{
"version": "1",
"default": "deny",
"tools": {
"code_execute": {
"limits": [
{
"counter": "code_execute_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}code_execute 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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Execute Python code in a sandboxed subprocess and return stdout/stderr. It is categorised as a Execute tool in the A-Modular-Kingdom MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the A-Modular-Kingdom MCP server in PolicyLayer and add a rule for code_execute: 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 A-Modular-Kingdom. Nothing to install.
code_execute 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 code_execute 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 code_execute. 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.
code_execute is provided by the A-Modular-Kingdom MCP server (masihmoafi/a-modular-kingdom). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from A-Modular-Kingdom, 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.
14 A-Modular-Kingdom tools catalogued and risk-classified — across an index of 43,000+ MCP servers.