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Jupyter Notebook MCP Server

Jupyter Notebook MCP Server

by shwetalsoni
OverviewSchemaSourceRelated ServersDiscussionsAdmin
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execute_notebook_cell

Execute a single cell in a Jupyter notebook by its index and return the outputs.

Instructions

Execute a specific cell in a Jupyter notebook.

Args:
    notebook_path: Absolute path to the .ipynb file
    cell_index: Index of the cell to execute (0-based)
    kernel_name: Jupyter kernel to use for execution
    timeout: Execution timeout in seconds

Returns:
    Execution result with outputs

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "notebook_path": "<notebook_path>",
      "cell_index": "<cell_index>"
    },
    "name": "execute_notebook_cell"
  }
}

Input Schema

NameRequiredDescriptionDefault
timeoutNo
cell_indexYes
kernel_nameNopython3
notebook_pathYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior2/5

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

With no annotations, the description must disclose behavior fully. It mentions execution and returns but does not cover side effects, synchronicity, timeout handling, or potential state changes. This lacks sufficient safety cues for an execution tool.

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?

The description is concise with clear sections for Args and Returns, no fluff. Every sentence adds value, and the main purpose is front-loaded.

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?

Covers 4 parameters and vague return ('Execution result with outputs'), but with no output schema, more detail on output structure or errors would improve completeness. Adequate but not extensive.

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

Parameters5/5

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

Schema description coverage is 0%, so the description's detailed Args section adds full meaning for each parameter (e.g., path, index, kernel, timeout). This compensates for the schema's lack of 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 specific cell in a Jupyter notebook,' identifying the resource (notebook cell) and action (execute). It differentiates from sibling tool 'execute_entire_notebook' by specifying 'specific 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 guidance on when to use this tool versus alternatives like 'execute_entire_notebook' or 'read_notebook_cells'. No context for prerequisites or exclusions is provided.

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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