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execute_temporary_code

Run temporary code blocks for debugging, magic commands, and intermediate variable inspection without saving to the notebook.

Instructions

Execute a temporary code block (not saved to the Notebook) and will return the output.

It will recommend to use in following cases:

  1. Execute Jupyter magic commands(e.g., %timeit, %pip install xxx)

  2. Debug code

  3. View intermediate variable values(e.g., print(xxx), df.head())

  4. Perform temporary statistical calculations(e.g., np.mean(df['xxx']))

DO NOT USE IN THE FOLLOWING CASES:

  1. Import new modules and perform variable assignments that affect subsequent Notebook execution

  2. Run code that requires a long time to run

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cell_contentYes
notebook_nameYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses that code is temporary (not saved) and returns output. The prohibitions imply that side effects like variable assignments do not persist, but this could be more explicit. Overall, the behavior is well communicated despite minor ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear opening sentence, a list of use cases, and a separate 'DO NOT USE' section. It is fairly concise, though slightly verbose with bullet lists. Front-loads the key behavior.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and 0% parameter coverage, the description provides adequate purpose and usage guidance. However, it lacks parameter details and does not describe the output format beyond 'return the output'. It sufficiently differentiates from siblings but misses some contextual details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not add any parameter-level information beyond field names. It does not explain what 'notebook_name' refers to or what format 'cell_content' expects. The description fails to compensate for the lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Execute a temporary code block (not saved to the Notebook)' with verb 'execute' and resource 'temporary code block'. It explicitly differentiates from siblings like 'execute_cell' which saves to the notebook, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use cases (Jupyter magic, debugging, intermediate values, temporary stats) and when-not-to-use cases (imports affecting notebook, long-running code). This guides the agent effectively on appropriate use versus alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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