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mcp-server-jupyter

mcp-server-jupyter

by ihrpr
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

read_output_of_cell

Retrieve the output of a specific Jupyter notebook cell by its ID, enabling programmatic inspection of cell results.

Instructions

Read the output of a specific cell in a notebook, by cell_id.

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": {
      "notebook_path": "<notebook_path>",
      "cell_id": "<cell_id>"
    },
    "name": "read_output_of_cell"
  }
}

Input Schema

NameRequiredDescriptionDefault
cell_idYes
notebook_pathYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.9

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavior. It only says 'read', which implies non-destructive, but does not explicitly state that the cell is not executed, nor does it describe what happens if the cell has no output or whether stderr is included. This leaves significant ambiguity for an agent.

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 of 10 words. Every word contributes to the meaning, with no redundancy or irrelevant detail.

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?

The tool has no output schema, no annotations, and only a two-parameter schema. The description does not cover return value format, when to use this tool vs siblings, or edge cases like a cell with no output. Core action is stated, but essential context for safe and effective use is missing.

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

Parameters2/5

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

The input schema has zero description coverage for parameters. The description clarifies that cell_id identifies the cell, and notebook_path is implied as the notebook location, but it does not explain format, constraints, or examples for either parameter. Minimal value is added over 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 clearly states the tool reads the output of a specific cell in a notebook, using cell_id as the selector. This explicitly differentiates it from sibling tools like read_notebook_with_outputs, which target entire notebooks, and execute_cell, which runs code.

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 phrase 'specific cell' implies this tool is for reading a single cell's output rather than a full notebook read, but it does not explicitly name when to use it over alternatives or provide exclusion criteria. Siblings are not referenced, leaving the agent to infer usage context.

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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