ipynb-mcp

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run_cell

Execute a single code cell in a Jupyter notebook's persistent kernel and save its outputs directly to the notebook, enabling incremental code evaluation with shared state.

Instructions

Execute one code cell in the notebook's persistent default kernel and save outputs.

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": {
      "path": "<path>",
      "index": "<index>"
    },
    "name": "run_cell"
  }
}

Input Schema

Name	Required	Description	Default
`path`	Yes
`index`	Yes
`timeout_seconds`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 30, 2026

TDQS

B3.3/5.0

Behavior3/5

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

No annotations are provided, so the description carries the disclosure burden. It does reveal an important behavioral trait—execution occurs in a persistent default kernel, implying state carries across calls—and mentions that outputs are saved. However, it omits side effects like arbitrary code execution risk, timeout behavior, or how failures affect kernel state.

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 a single front-loaded sentence with no filler. Every word earns its place: it names the action, the resource, the execution context, and the persistence side effect.

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

Completeness2/5

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

Although an output schema exists, the description still misses critical context: what path/index identify, timeout semantics, and the side effects of running code in a persistent kernel. It is too sparse to fully support correct invocation for a stateful execution tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description offers no explanation of path, index, or timeout_seconds. The agent must infer from parameter names alone, which is insufficient for a 3-parameter tool with no enums or detailed 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 states a specific verb ('Execute') and resource ('one code cell'), and adds distinguishing qualifiers: the notebook's persistent default kernel and saving outputs. This clearly separates it from sibling tools like read_cell, edit_cell, and restart_kernel.

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 the tool by its action wording, but it gives no explicit conditions or alternatives. It does not say when to prefer restart_kernel, edit_cell, or read_cell, leaving usage guidance to inference.

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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