jupyter-editor-mcp

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ipynb_search_notebooks

Search across multiple Jupyter Notebook files using regex patterns, with optional context retrieval for matching results.

Instructions

Search across multiple Jupyter Notebooks (.ipynb).

Args: ipynb_filepaths: List of notebook paths (absolute paths preferred) pattern: Search pattern (regex) return_context: Whether to include context

Returns: Dict with 'results' and 'match_count' 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>",
      "pattern": "<pattern>"
    },
    "name": "ipynb_search_notebooks"
  }
}

Input Schema

Name	Required	Description	Default
`pattern`	Yes
`return_context`	No
`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

A3.9/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses the return structure (results/match_count/error) and a practical note about absolute paths, but it does not explicitly confirm the tool is read-only or mention any side effects or prerequisites. Some behavioral info is present, but not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a brief purpose line followed by an Args section and a Returns section. Every line provides useful information, though there is some vertical whitespace. It is concise and front-loaded.

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 output schema exists, so return values are formally defined. The description gives the purpose, all parameter explanations, and a summary of the return keys. It lacks details on edge cases or limitations, but for a search tool with an output schema, this is sufficient.

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?

With 0% schema coverage, the description compensates by explaining each parameter: 'ipynb_filepaths' gets a clarification of absolute paths preferred, 'pattern' is identified as a regex, and 'return_context' is described as a toggle for context. This adds meaningful semantics beyond the bare schema types.

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 action ('Search') and the resource ('multiple Jupyter Notebooks'), and the scope is distinct from siblings like ipynb_search_cells which operates on individual cells. It immediately conveys what the tool does.

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 when to use it (searching across notebooks), but it does not explicitly state when not to use it or mention alternatives such as ipynb_search_cells for cell-level search. The context is clear but lacks exclusion guidance.

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