jupyter-editor-mcp

by jsamuel1

Overview Schema Source Related Servers Discussions Admin

Python

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ipynb_get_metadata

Retrieve metadata from Jupyter Notebook files or specific cells by providing the file path and optional cell index.

Instructions

Get Jupyter Notebook (.ipynb) or cell metadata.

Args: ipynb_filepath: Path to Jupyter Notebook (.ipynb) file (absolute path preferred) cell_index: Index of cell (None for notebook metadata)

Returns: Metadata dictionary or dict with 'error' key

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_get_metadata"
  }
}

Input Schema

Name	Required	Description	Default
`cell_index`	No
`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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It indicates the return type (metadata dictionary) and the possibility of an 'error' key, which hints at error behavior. The read-only nature is implied by 'Get', and the cell_index semantics are explained. However, edge cases like invalid index or missing file are not detailed.

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 well-structured with a one-line summary followed by Args and Returns sections. It is concise, front-loaded with the purpose, and every line adds value without unnecessary detail.

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?

The tool is relatively simple with two parameters, and the description explains both plus the return behavior. An output schema exists, so detailed return keys are not needed. Minor omissions like whether cell_index is zero-based prevent a perfect score, but overall the context is sufficient.

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?

The input schema provides only type and default, but the description adds significant meaning: 'absolute path preferred' for ipynb_filepath and '(None for notebook metadata)' for cell_index. This fully compensates for the 0% schema description coverage and gives the agent precise usage info.

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 the tool retrieves Jupyter Notebook or cell metadata, using a specific verb ('Get') and resource. It distinguishes from sibling tools like ipynb_get_cell that retrieve cell content rather than metadata.

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 usage is implied: use this tool to fetch metadata from a notebook or cell. However, it does not explicitly mention when to use this over alternatives such as ipynb_get_notebook_info or ipynb_get_cell, nor does it provide exclusions.

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