jupyter-editor-mcp

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ipynb_read_notebook

Read a Jupyter Notebook file to return its structure summary, including cell count, cell types, kernel info, and format version.

Instructions

Read a Jupyter Notebook (.ipynb) and return structure summary.

Args: ipynb_filepath: Path to Jupyter Notebook (.ipynb) file (absolute path preferred)

Returns: Dictionary with cell_count, cell_types, kernel_info, format_version or dict with 'error' key on failure

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": {
      "ipynb_filepath": "<ipynb_filepath>"
    },
    "name": "ipynb_read_notebook"
  }
}

Input Schema

Name	Required	Description	Default
`ipynb_filepath`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.2Aug 7, 2026

TDQS

A4.3/5.0

Behavior4/5

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

No annotations exist, so the description carries full responsibility. It discloses the return format (dict with cell_count, cell_types, kernel_info, format_version), mentions the error key on failure, and notes the preference for absolute paths. This is solid transparency for a read-only 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 and well-structured with Args and Returns sections. Every sentence provides value, no fluff, and the key information is front-loaded in the first sentence.

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

Completeness5/5

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

For a single-parameter read tool with an output schema, the description covers the essential aspects: purpose, parameter meaning, return structure, and failure behavior. It is complete and self-contained.

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%, but the description compensates by explaining 'ipynb_filepath' as the path to a Jupyter Notebook file and recommends an absolute path. This adds meaningful context beyond the schema's bare string type.

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 it reads a Jupyter Notebook and returns a structure summary. The verb 'Read' and resource 'Jupyter Notebook (.ipynb)' are specific, and the summary output distinguishes it from sibling tools focused on editing or cell-level operations.

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

Usage Guidelines3/5

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

The description implies use when a structure summary is needed, but it does not explicitly mention alternatives or when not to use it. With many sibling tools like ipynb_get_notebook_info or ipynb_list_cells, it would benefit from explicit differentiation, but the context is clear enough for basic selection.

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