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shwetalsoni

Jupyter Notebook MCP Server

by shwetalsoni

add_cell_to_notebook

Insert a new code, markdown, or raw cell into a Jupyter notebook at a specified position with optional metadata.

Instructions

Add a new cell to a Jupyter notebook.

Args:
    notebook_path: Absolute path to the .ipynb file
    cell_content: Content of the new cell
    cell_type: Type of cell ('code', 'markdown', 'raw')
    position: Position to insert cell (default: append to end)
    metadata: Optional cell metadata

Returns:
    Status of the operation

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
metadataNo
positionNo
cell_typeNocode
cell_contentYes
notebook_pathYes
Behavior2/5

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

With no annotations, the description fails to disclose important behavioral traits such as whether the tool modifies the notebook file (destructive hint missing), any side effects, or error behavior. The return value is vague ('Status of the operation') with no format or possible values.

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?

Description is concise and well-structured: one-line purpose, then Args and Returns sections. Every sentence is necessary and no redundant information. Appropriate length for five parameters.

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 the complexity (5 parameters, no annotations, no output schema), the description covers basic purpose and parameter meanings but lacks critical contextual details like what happens if notebook_path does not exist, return value structure, error handling, or permissions required. The description is adequate but not complete.

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?

Schema coverage is 0%, so the description carries the full burden. The Args section provides meaningful explanations for all five parameters including types, defaults, and enum values for cell_type. This adds significant value beyond the schema, though position indexing (0-based vs 1-based) and metadata format are not specified.

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?

Description clearly states 'Add a new cell to a Jupyter notebook' with a specific verb and resource. It is distinct from sibling tools like execute_entire_notebook, execute_notebook_cell, get_notebook_info, and read_notebook_cells, which handle different operations.

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 guidance on when to use this tool versus alternatives. Does not mention prerequisites (e.g., notebook must exist) or scenarios where another tool would be preferred. The description only states what the tool does, not when to apply it.

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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