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jupyter_mcp

jupyter_mcp

by Liu-Eroteme
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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search_cells

Search through Jupyter notebook cells by source, name, summary, or output. Find matching cells and lines using text or regex queries.

Instructions

Search cell sources, names, summaries, and condensed outputs. Returns matching cells with the matching lines.

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>",
      "query": "<query>"
    },
    "name": "search_cells"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYes
queryYes
regexNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

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

No annotations are provided, so the description must disclose behavioral traits. It states that the tool returns 'matching cells with the matching lines', which is straightforward. However, it does not explain what 'condensed outputs' means or whether the operation is read-only or has side effects.

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 concise at two sentences, front-loading the purpose. Every sentence provides value without extraneous information.

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?

Despite having an output schema, the description lacks explanation for parameters and does not clarify the structure of results beyond 'matching lines'. Given the complexity of the tool (3 parameters, 0% schema coverage), the description is insufficient for complete understanding.

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

Parameters1/5

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

The schema has 0% description coverage, and the tool description does not explain any of the three parameters (path, query, regex). An agent cannot determine the meaning of 'path' or the format of 'query', making the tool difficult to use correctly.

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 specifies the verb 'search' and the resource 'cells', and details what is searched (sources, names, summaries, condensed outputs). It distinguishes this tool from siblings like 'read_cells' or 'summarize_cells' by focusing on searching across cell content.

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?

The description does not provide any guidance on when to use this tool versus alternatives (e.g., 'read_cells'). It lacks context on prerequisites, limitations, or conditions that would help an agent decide.

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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