Skip to main content
Glama

cell_add

Add a new cell to any Jupyter notebook. Specify the cell content, type (code, markdown, or raw), and insertion position.

Instructions

Add a new cell to a notebook.

  • name: notebook name (with or without .ipynb)

  • source: the cell content / code

  • cell_type: 'code', 'markdown', or 'raw' (default: 'code')

  • position: index to insert at (0 = first cell, -1 = append at end) Returns the new cell's ID and position.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
sourceYes
cell_typeNocode
positionNo
Behavior3/5

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

The description adds moderate value beyond annotations by stating the return value (cell ID and position) and specifying defaults. However, it does not disclose error handling, required permissions, or behavior on invalid inputs, which are important for a mutation tool.

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 short, uses bullet-like formatting for clarity, and front-loads the purpose. Every sentence is essential, with no redundancy or fluff.

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?

Given the tool's simplicity (4 parameters, no output schema), the description covers purpose, parameters, and return value. It lacks information about prerequisites (e.g., notebook must exist) and error conditions, but overall it is fairly complete for an add operation.

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

Parameters5/5

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

With 0% schema description coverage, the description compensates fully by explaining each parameter: name format, source content, cell_type options with default, and position semantics (0 = first, -1 = append). This adds significant meaning beyond the schema's type and defaults.

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 action 'Add a new cell' and the target resource 'notebook', making the purpose unambiguous. It differentiates from sibling tools like cell_delete and cell_update by focusing on addition.

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 implies usage for adding cells but provides no explicit guidance on when to use this tool versus alternatives, nor does it mention prerequisites or exclusion criteria. The context is implied by the tool's function.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Try3D/JupyterMCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server