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jupyter_mcp

jupyter_mcp

by Liu-Eroteme
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

summarize_cells

Generate detailed per-cell summaries of Jupyter notebooks, with optional output summaries, to quickly orient users in large notebooks.

Instructions

Detailed summaries (LLM): per-cell description plus, optionally, a summary of each cell's current output. Cheaper than reading full cells when orienting in a large notebook.

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>"
    },
    "name": "summarize_cells"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYes
namesNo
include_outputsNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description must convey behavioral traits. It mentions 'LLM' hinting at AI-generation, but does not state that the tool is read-only, does not modify cells, or require special permissions. Important behavioral context (e.g., latency, non-destructive nature) is missing.

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?

The description is concise with two sentences, front-loading the main purpose in the first sentence. However, it could be more structured (e.g., using bullet points) to improve skimmability, and the second sentence appears slightly fragmented. Overall, no wasted words.

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?

Given three parameters and an existing output schema, the description is incomplete. It does not cover the 'path' or 'names' parameters, nor does it describe the output structure (what the 'detailed summaries' contain). Error handling, limitations (e.g., notebook size), and return fields are absent, leaving an agent underinformed.

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 0% description coverage, so the description must compensate. It explains that include_outputs controls whether cell output summaries are included, but it does not describe the 'path' or 'names' parameters. For example, it does not clarify that 'names' likely refers to cell identifiers. The output schema exists but is not described, leaving gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides 'detailed summaries (LLM): per-cell description plus, optionally, a summary of each cell's current output.' It specifies the verb (summarize) and resource (cells), and hints at a cheaper alternative to reading full cells, which differentiates it from read_cells. However, it could more explicitly distinguish from sibling tools like notebook_overview.

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 mentions 'Cheaper than reading full cells when orienting in a large notebook,' giving implied context for when to use it. However, it does not explicitly state when not to use it or name specific alternatives, leaving some ambiguity for an AI agent.

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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