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Jupyter Notebook MCP Server

Jupyter Notebook MCP Server

by shwetalsoni
OverviewSchemaSourceRelated ServersDiscussionsAdmin
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read_notebook_cells

Read cells from a Jupyter notebook, optionally filtering by type (code, markdown, raw). Returns cell content and metadata.

Instructions

Read cells from a Jupyter notebook.

Args:
    notebook_path: Absolute path to the .ipynb file
    cell_type: Optional filter by cell type ('code', 'markdown', 'raw')

Returns:
    List of cell dictionaries with metadata

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>"
    },
    "name": "read_notebook_cells"
  }
}

Input Schema

NameRequiredDescriptionDefault
cell_typeNo
notebook_pathYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations provided, the description must disclose behavioral traits. It explains the read-only nature and return format (list of cell dictionaries with metadata), but omits details about file validation, error handling, or performance implications. 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?

The description is concise with a clear structure: a one-sentence purpose, then Args and Returns sections. Every sentence adds value, and there is no redundant information.

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?

Given the tool's low complexity (2 parameters, no output schema, no annotations), the description covers main functionality, parameters, and return format. However, it is slightly incomplete by not addressing scenarios like non-existent files or empty results, but overall sufficient for basic use.

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 full responsibility. It explains notebook_path as an absolute path and cell_type as an optional filter with allowed values ('code', 'markdown', 'raw'), adding meaning beyond the schema. Missing explanation of default behavior (returns all cells when filter is null).

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 'Read cells from a Jupyter notebook', which is a specific verb+resource. It distinguishes itself from sibling tools like add_cell_to_notebook, execute_notebook_cell, and get_notebook_info by focusing on read-only access.

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?

The description provides no guidance on when to use this tool versus alternatives. It lacks explicit usage context, exclusions, or references to other tools, leaving the agent without decision support.

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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