jupyter-editor-mcp

by jsamuel1

Overview Schema Source Related Servers Discussions Admin

Python

Local

ipynb_clear_outputs

Remove all outputs from code cells in one or more Jupyter notebook files, making them easier to share or version.

Instructions

Clear all outputs from code cells in one or more Jupyter Notebooks (.ipynb).

Args: ipynb_filepaths: Single filepath or list of filepaths (absolute paths preferred)

Returns: Dict with 'success' and 'notebooks_processed' or '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_filepaths": "<ipynb_filepaths>"
    },
    "name": "ipynb_clear_outputs"
  }
}

Input Schema

Name	Required	Description	Default
`ipynb_filepaths`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.2Aug 7, 2026

TDQS

A3.7/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states the action and return format but does not disclose that clearing outputs modifies the notebook files in place, is irreversible, or how file errors are handled. This is a significant gap for a mutation 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 (one purpose sentence plus Args/Returns) and front-loaded. Every sentence adds value, and the structure is easy to scan.

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?

For a simple one-parameter tool, the description covers purpose, parameter format, and return shape. However, it omits important context such as in-place file mutation, potential batch partial-failure behavior, and any preconditions. Given the lack of annotations, this leaves the agent underinformed about side effects.

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?

The schema has a single parameter with 0% coverage, but the description adds meaningful details: 'Single filepath or list of filepaths' clarifies the anyOf union, and 'absolute paths preferred' gives practical guidance. This goes beyond the bare schema.

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 uses a specific verb 'Clear' and precise resource 'all outputs from code cells in one or more Jupyter Notebooks'. It clearly distinguishes from sibling tools that target metadata, kernels, or cell editing.

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 intended use is implied by the description ('clear outputs'), but there is no explicit guidance on when to prefer this over alternatives like ipynb_apply_to_notebooks or ipynb_validate_notebooks_batch. No exclusions or prerequisites are mentioned.

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