execute_code
Destructive
Run code directly in Jupyter kernels or sandboxes for quick tests, debugging, magic commands, and shell commands without modifying notebooks.
Instructions
Execute code directly in a kernel (not saved to notebook).
If `use_sandbox` selected an active sandbox, this tool executes on that
sandbox instead of a Jupyter kernel. This allows agents to switch between
kernel-backed and sandbox-backed execution using the same execute_code API.
Targets the current activated notebook's kernel by default. Pass kernel_id
to execute in a specific kernel directly — including raw kernels with no
notebook attached.
Recommended to use in following cases:
1. Execute Jupyter magic commands(e.g., `%timeit`, `%pip install xxx`)
2. Performance profiling and debugging.
3. View intermediate variable values(e.g., `print(xxx)`, `df.head()`)
4. Temporary calculations and quick tests(e.g., `np.mean(df['xxx'])`)
5. Execute Shell commands in Jupyter server(e.g., `!git xxx`)
Under no circumstances should you use this tool to:
1. Import new modules or perform variable assignments that affect subsequent Notebook execution
2. Execute dangerous code that may harm the Jupyter server or the user's data without permission
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | Code to execute (supports magic commands with %, shell commands with !) | |
| timeout | No | Maximum seconds to wait for execution (0 = use config default) | |
| kernel_id | No | Target an existing kernel by ID (e.g. a raw kernel with no notebook). If omitted, uses the current notebook's kernel. |