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ipynb-mcp

ipynb-mcp

by YmSaki
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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read_cell

Retrieve a specific Jupyter notebook cell by its zero-based index, with code outputs included, to inspect or verify cell contents directly.

Instructions

Read one cell by its zero-based index, including outputs for code cells.

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": "read_cell"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYes
indexYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

No annotations are present, so the description carries the burden of signaling behavior. The word 'Read' indicates a non-destructive operation, and 'including outputs for code cells' tells the agent that stored code cell outputs are part of the result. It does not describe error behavior for invalid indexes, but this is acceptable for a simple read 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?

A single sentence that front-loads the action, the target resource, and the key behavioral detail about code cell outputs. There is no wasted wording.

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?

With only two parameters and an output schema available, the description covers the essential call semantics and the notable output behavior. The main residual gap is the unstated role of `path`, which prevents a perfect score.

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

Parameters3/5

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

The phrase 'zero-based index' adds meaningful semantics to the `index` parameter, which is valuable given 0% schema description coverage. However, `path` is left unexplained, so an agent must infer whether it refers to a notebook path, a file path, or something else.

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?

States a specific action ('Read'), a specific resource ('one cell'), and a precise addressing scheme ('zero-based index'). It also notes that outputs are included for code cells, which helps distinguish it from mutation siblings like add_cell, edit_cell, and run_cell.

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 read verb plus sibling names implies this is the non-mutating lookup counterpart to add_cell/edit_cell/run_cell, but the description never explicitly says when to prefer read_cell over notebook_info or when not to use it. Usage context is implied rather than clearly stated.

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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