run_kernel_vm
Run a ChainGraph decision kernel's compute(policy_parameters) inside a sandboxed, deterministic, in-browser QuickJS-ng WebAssembly VM (ocg-deterministic-compute@2) and return its output_payload. Demo kernel set only -- for the full catalog, use the worker's compute kernels directly. Renders the i...
This record as markdown: /tools/postoaklabs-ainumbers-mcp-apps/run-kernel-vm.md
What run_kernel_vm does on Ainumbers Mcp Apps
AI agents invoke run_kernel_vm to trigger actions in Ainumbers Mcp Apps. 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 |
|---|---|---|---|
inputs | object | — | Map of tool input element IDs to values (see manifest input_schema). Applied via AIN Bridge prefill. |
Parameters from the server's own tool schema.
Why run_kernel_vm is rated High
Executes arbitrary decision kernels with parameters in WebAssembly; outputs depend entirely on kernel logic and inputs.
From the tool's definition Run ChainGraph decision kernel's compute inside sandboxed VM, returns output_payload
Attacks that exploit this kind of access
The rule that runs run_kernel_vm safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Ainumbers Mcp Apps, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For run_kernel_vm, this is the rule to start with:
run_kernel_vm 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 Ainumbers Mcp Apps, apply this rule, and every run_kernel_vm call is checked against it from then on.
Questions about run_kernel_vm
Run a ChainGraph decision kernel's compute(policy_parameters) inside a sandboxed, deterministic, in-browser QuickJS-ng WebAssembly VM (ocg-deterministic-compute@2) and return its output_payload. Demo kernel set only -- for the full catalog, use the worker's compute kernels directly. Renders the interactive AINumbers tool as a widget; inputs are applied via the AIN Bridge and the tool runs client-side (zero PII, zero network). Output schema: call describe_tool("run_kernel_vm"). It is categorised as a Execute tool in the Ainumbers Mcp Apps MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
run_kernel_vm accepts 1 parameter: inputs. The full parameter table on this page comes from the server's own tool schema.
Register the Ainumbers Mcp Apps MCP server in PolicyLayer and add a rule for run_kernel_vm: 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 Ainumbers Mcp Apps. Nothing to install.
run_kernel_vm 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 run_kernel_vm 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 run_kernel_vm. 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.
run_kernel_vm is provided by the Ainumbers Mcp Apps MCP server (postoaklabs/ainumbers-mcp-apps). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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