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Jupyter MCP Server

Jupyter MCP Server

by ChengJiale150
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read_cell

Retrieve the source code, execution count, and output of a specific cell in a Jupyter notebook.

Instructions

Read the detailed content of a specific cell. It will return the source code, execution count and output of the cell.

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_name": "<notebook_name>",
      "cell_index": "<cell_index>"
    },
    "name": "read_cell"
  }
}

Input Schema

NameRequiredDescriptionDefault
cell_indexYesCell index(0-based)
notebook_nameYes
return_outputNoWhether to return output

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior2/5

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

No annotations provided, so description must cover behavior. It mentions return fields but does not disclose side effects (likely none), prerequisites (e.g., notebook must exist), or whether operation is idempotent. Lacks details on behavior beyond outputs.

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?

Two sentences, front-loaded with purpose, no unnecessary words. Efficiently communicates key information.

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?

For a read tool with no output schema and no annotations, description adequately states what is returned. Could mention error conditions (invalid cell_index, missing notebook) but overall sufficient for typical use.

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?

Schema has 67% description coverage. Description adds 'source code, execution count and output' which relates to return_output parameter but does not elaborate on notebook_name or cell_index beyond schema. Baseline score appropriate as description adds minimal extra meaning.

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 a specific cell and returns source code, execution count, and output. It differentiates from sibling tools like execute_cell or delete_cell by focusing on non-modifying read operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs alternatives. With 11 sibling tools, explicit context about when to choose read_cell over list_cell or execute_cell would be helpful.

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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