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jupyter_mcp

jupyter_mcp

by Liu-Eroteme
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

inspect_variable

Inspect a live kernel variable to view its type, shape, schema, length, and richest Jupyter representation without adding a notebook cell.

Instructions

Inspect a live kernel variable without adding a cell: type, shape, schema/columns, length, plus its richest Jupyter repr — dataframes condense to a CSV table, figures come back as images, everything else falls back to a (pretty) repr.

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>",
      "variable": "<variable>"
    },
    "name": "inspect_variable"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYes
variableYes
timeout_secondsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, description fully owns disclosure. It specifies how different data types are handled (dataframes->CSV, figures->images, else repr), which is good transparency.

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?

Single sentence that efficiently conveys the core purpose and behavior. Could be broken into clearer sections, but no waste.

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?

Lacks parameter details and no output schema. However, it covers return behavior well. Adequate but not fully complete given the tool's complexity.

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?

Schema has 0% description coverage; description does not explain what 'path' and 'variable' mean, nor the timeout. It adds no semantic value beyond the parameter names and default.

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?

Clearly states it inspects a live kernel variable, listing specific attributes (type, shape, schema/columns, length, Jupyter repr). Distinct from all sibling tools which are cell or notebook operations.

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?

Mentions 'without adding a cell' implying lightweight inspection, but does not explicitly state when to use this tool versus alternatives or when not to use it.

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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