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am-3

universal-notebook-mcp

by am-3

notebook_run_range

Execute a specific range of cells in a Jupyter notebook and retrieve their outputs, with configurable error handling and output saving.

Instructions

Execute cells from start to end (inclusive) and return all outputs.

Execution stops at the first error by default (stop_on_error=true).

Args: notebook_path: Path to the .ipynb file, relative to the workspace root. start: First cell index to run (inclusive). end: Last cell index to run (inclusive). kernel_name: Kernel to use (default: 'python3'). timeout: Per-cell timeout in seconds (default: 60). stop_on_error: Stop at first failing cell (default: true). save_outputs: Write outputs back to the .ipynb file (default: true).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endYes
startYes
timeoutNo
kernel_nameNo
save_outputsNo
notebook_pathYes
stop_on_errorNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Describes default stop_on_error, timeout, and save_outputs behavior. Lacks details on kernel instantiation, side effects (e.g., kernel state), and output format. Adequate but not thorough.

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

Conciseness5/5

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

Two lead sentences plus a clear bulleted arg list. Every sentence adds value; no redundancy. Well-structured for quick scanning.

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

Completeness4/5

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

Covers core functionality and all parameters. Does not describe output schema or kernel prerequisites, but output schema exists externally. Slightly incomplete for a parameter-rich tool.

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

Parameters4/5

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

With 0% schema coverage, the description explains all 7 parameters with defaults and semantics (e.g., 'relative to workspace root,' 'per-cell timeout'). Adds value but lacks some format constraints.

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 cells from start to end (inclusive) and return all outputs,' specifying verb, object, and range. This distinguishes it from siblings like notebook_run_all and notebook_run_cell.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives (e.g., notebook_run_all, notebook_run_cell). Does not explain trade-offs or exclusion conditions.

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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