An MCP server developed for AI to connect and manage Jupyter Notebooks in real-time
Developed by Datalayer
Breaking change in v1.0.0: You must configure MCP_TOKEN in your MCP client setup.
For setup details, see: https://jupyter-mcp-server.datalayer.tech/providers/jupyter-streamable-http-standalone/#3-configure-your-mcp-client
Update in v1.0.2: pycrdt is now supported, so installing datalayer_pycrdt is no longer required.
We Need Your Feedback!
We're actively developing support for JupyterHub and Google Colab deployments. If you're using or planning to use Jupyter MCP Server with these platforms, we'd love to hear from you!
🏢 JupyterHub users: Share your deployment setup and requirements
🌐 Google Colab users: Help us understand your use cases and workflows
Join the conversation in our Community page - your feedback will help us prioritize features and ensure these integrations work seamlessly for your needs.
Related MCP server: mcp-server-jupyter
⚡ Real-time control: Instantly view notebook changes as they happen.
🔁 Smart execution: Automatically adjusts when a cell run fails thanks to cell output feedback.
🧠 Context-aware: Understands the entire notebook context for more relevant interactions.
📊 Multimodal support: Support different output types, including images, plots, and text.
📚 Multi-notebook support: Seamlessly switch between multiple notebooks.
🎨 JupyterLab integration: Enhanced UI integration like automatic notebook opening.
🤝 MCP-compatible: Works with any MCP client, such as Claude Desktop, Cursor, Windsurf, and more.
🔍 Observability: Built-in hook system with OpenTelemetry integration for tracing tool calls and kernel executions.
Compatible with any Jupyter deployment (local, JupyterHub, ...) and with Datalayer hosted Notebooks.
The server provides a rich set of tools for interacting with Jupyter notebooks, categorized as follows. For more details on each tool, their parameters, and return values, please refer to the official Tools documentation.
Name | Description |
| List files and directories in the Jupyter server's file system. |
| List all available and running kernel sessions on the Jupyter server. |
| Connect to a Jupyter server dynamically without restarting the MCP server. Not available when running as Jupyter extension. Useful for switching servers dynamically or avoiding hardcoded configuration. Read more |
Name | Description |
| Connect to a notebook file, create a new one, or switch between notebooks. |
| List all notebooks available on the Jupyter server and their status |
| Restart the kernel for a specific managed notebook. |
| Disconnect from a specific notebook and release its resources. |
| Read notebook cells source content with brief or detailed format options. |
Name | Description |
| Read the full content (Metadata, Source and Outputs) of a single cell. |
| Insert a new code or markdown cell at a specified position. |
| Delete a cell at a specified index. |
| Move a cell from one position to another within a notebook. |
| Overwrite the source code of an existing cell. |
| Apply surgical find-and-replace edits to a cell's source without full rewrite. |
| Execute a cell with timeout, supports multimodal output including images. |
| Insert a new code cell and execute it in one step. |
| Execute code directly in the kernel, supports magic commands and shell commands. |
Available only when JupyterLab mode is enabled. It is enabled by default.
When running in JupyterLab mode, Jupyter MCP Server integrates with jupyter-mcp-tools to expose additional JupyterLab commands as MCP tools. By default, the following tools are enabled:
Name | Description |
| Execute all cells in the current notebook sequentially |
| Get information about the currently selected cell |
You can now customize which tools from jupyter-mcp-tools are available using the allowed_jupyter_mcp_tools configuration parameter. This allows you to enable additional notebook operations, console commands, file management tools, and more.
# Example: Enable additional tools via command-line
jupyter lab --port 4040 --IdentityProvider.token MY_TOKEN --JupyterMCPServerExtensionApp.allowed_jupyter_mcp_tools="notebook_run-all-cells,notebook_get-selected-cell,notebook_append-execute,console_create"For the complete list of available tools and detailed configuration instructions, please refer to the Additional Tools documentation.
The server also supports prompt feature of MCP, providing a easy way for user to interact with Jupyter notebooks.
Name | Description |
| Cite specific cells from specified notebook (like |
For more details on each prompt, their input parameters, and return content, please refer to the official Prompt documentation.
For comprehensive setup instructions—including Streamable HTTP transport, running as a Jupyter Server extension and advanced configuration—check out our documentation. Or, get started quickly with JupyterLab and STDIO transport here below.
pip install jupyterlab==4.4.1 jupyter-collaboration==4.0.2 jupyter-mcp-tools>=0.1.4 ipykernel pycrdtTo confirm your environment is correctly configured:
Open a notebook in JupyterLab
Type some content in any cell (code or markdown)
Observe the tab indicator: you should see an "×" appear next to the notebook name, indicating unsaved changes
Wait a few seconds—the "×" should automatically change to a "●" without manually saving
This automatic saving behavior confirms that the real-time collaboration features are working properly, which is essential for MCP server integration.
# Start JupyterLab on port 8888, allowing access from any IP and setting a token
jupyter lab --port 8888 --IdentityProvider.token MY_TOKEN --ip 0.0.0.0If you are running notebooks through JupyterHub instead of JupyterLab as above, refer to ourJupyterHub setup guide.
Next, configure your MCP client to connect to the server. We offer two primary methods—choose the one that best fits your needs:
📦 Using uvx (Recommended for Quick Start): A lightweight and fast method using uv. Ideal for local development and first-time users.
🐳 Using Docker (Recommended for Production): A containerized approach that ensures a consistent and isolated environment, perfect for production or complex setups.
First, install uv:
pip install uv
uv --version
# should be 0.6.14 or higherSee more details on uv installation.
Then, configure your client:
{
"mcpServers": {
"jupyter": {
"command": "uvx",
"args": ["jupyter-mcp-server@latest"],
"env": {
"JUPYTER_URL": "http://localhost:8888",
"JUPYTER_TOKEN": "MY_TOKEN",
"ALLOW_IMG_OUTPUT": "true"
}
}
}
}On macOS and Windows:
{
"mcpServers": {
"jupyter": {
"command": "docker",
"args": [
"run", "-i", "--rm",
"-e", "JUPYTER_URL",
"-e", "JUPYTER_TOKEN",
"-e", "ALLOW_IMG_OUTPUT",
"datalayer/jupyter-mcp-server:latest"
],
"env": {
"JUPYTER_URL": "http://host.docker.internal:8888",
"JUPYTER_TOKEN": "MY_TOKEN",
"ALLOW_IMG_OUTPUT": "true"
}
}
}
}On Linux:
{
"mcpServers": {
"jupyter": {
"command": "docker",
"args": [
"run", "-i", "--rm",
"-e", "JUPYTER_URL",
"-e", "JUPYTER_TOKEN",
"-e", "ALLOW_IMG_OUTPUT",
"--network=host",
"datalayer/jupyter-mcp-server:latest"
],
"env": {
"JUPYTER_URL": "http://localhost:8888",
"JUPYTER_TOKEN": "MY_TOKEN",
"ALLOW_IMG_OUTPUT": "true"
}
}
}
}Port Configuration: Ensure the port in your Jupyter URLs matches the one used in the jupyter lab command. For simplified config, set this in JUPYTER_URL.
Server Separation: Use JUPYTER_URL when both services are on the same server, or set individual variables for advanced deployments. The different URL variables exist because some deployments separate notebook storage (DOCUMENT_URL) from kernel execution (RUNTIME_URL).
Authentication: In most cases, document and runtime services use the same authentication token. Use JUPYTER_TOKEN for simplified config or set DOCUMENT_TOKEN and RUNTIME_TOKEN individually for different credentials.
Notebook Path: The DOCUMENT_ID parameter specifies the path to the notebook the MCP client default to connect. It should be relative to the directory where JupyterLab was started. If you omit DOCUMENT_ID, the MCP client can automatically list all available notebooks on the Jupyter server, allowing you to select one interactively via your prompts.
Image Output: Set ALLOW_IMG_OUTPUT to false if your LLM does not support mutimodel understanding.
For detailed instructions on configuring various MCP clients—including Claude Desktop, VS Code, Cursor, Cline, and Windsurf — see the Clients documentation.
Interact with LLMs that supports multimodal input (like Gemini 2.5 Pro) to fully utilize advanced multimodal understanding capabilities.
Use a MCP client that supports returning image data and can parse it (like Cursor, Gemini CLI, etc.), as some clients may not support this feature.
Break down complex task (like the whole data science workflow) into multiple sub-tasks (like data cleaning, feature engineering, model training, model evaluation, etc.) and execute them step-by-step.
Provide clearly structured prompts and rules (👉 Visit our Prompt Templates to get started)
Provide as much context as possible (like already installed packages, field explanations for existing datasets, current working directory, detailed task requirements, etc.).
We welcome contributions of all kinds! Here are some examples:
🐛 Bug fixes
📝 Improvements to existing features
🔧 New feature development
📚 Documentation improvements and prompt templates
For detailed instructions on how to get started with development and submit your contributions, please see our Contributing Guide.
Looking for blog posts, videos, or other materials about Jupyter MCP Server?
👉 Visit the Resources section in our documentation for more!
If this project is helpful to you, please give us a ⭐️
Made with ❤️ by Datalayer
A hosted deployment is available on Fronteir AI.
Clear the outputs and execution count of a single code cell in the currently activated notebook, without deleting the cell itself.
| Name | Required | Description | Default |
|---|---|---|---|
| cell_index | Yes | Index of the code cell to clear (0-based) |
| Name | Required | Description |
|---|---|---|
| result | Yes | Success message with the number of outputs removed |
Connect to a Jupyter server dynamically with URL and token.
This tool allows you to connect to different Jupyter servers without needing to
restart the MCP server or modify configuration files. Particularly useful when:
- Working with multiple Jupyter servers with different ports/tokens
- Jupyter server token changes dynamically
- Need to switch between different Jupyter instances
Example usage:
- "Connect to http://localhost:8888 with token abc123"
- "Connect to http://localhost:8889 without authentication"
| Name | Required | Description | Default |
|---|---|---|---|
| provider | No | Provider type | jupyter |
| jupyter_url | Yes | Jupyter server URL to connect to (e.g., 'http://localhost:8888') | |
| jupyter_token | No | Jupyter server authentication token |
| Name | Required | Description |
|---|---|---|
| result | Yes | Connection status message |
Delete specific cells from the currently activated notebook and return the cell source of deleted cells (if include_source=True).
| Name | Required | Description | Default |
|---|---|---|---|
| cell_indices | Yes | List of cell indices to delete (0-based) | |
| include_source | No | Whether to include the source of deleted cells |
| Name | Required | Description |
|---|---|---|
| result | Yes | Success message with list of deleted cells and their source (if include_source=True) |
Perform a surgical find-and-replace within a cell's source (like an editor's Edit tool).
Finds old_string in the cell and replaces it with new_string. Matching is literal
(not regex) and may span multiple lines. By default, old_string must appear exactly once;
set replace_all=True for multiple occurrences. Returns a diff of the changes made.
Prefer this over overwrite_cell_source for small, targeted edits — it is safer because
unchanged parts of the cell are left untouched. Use read_cell first to see the current
source and construct an accurate old_string.| Name | Required | Description | Default |
|---|---|---|---|
| cell_index | Yes | Index of the cell to edit (0-based) | |
| new_string | Yes | Replacement string | |
| old_string | Yes | Exact string to find in cell source | |
| replace_all | No | Replace all occurrences (default: first only) |
| Name | Required | Description |
|---|---|---|
| result | Yes | Success message with diff showing changes made |
Execute a cell from the currently activated notebook with timeout and return it's outputs
| Name | Required | Description | Default |
|---|---|---|---|
| stream | No | Enable streaming progress (including time indicator) updates for long-running cells | |
| timeout | No | Maximum seconds to wait for execution (0 = use config default) | |
| cell_index | Yes | Index of the cell to execute (0-based) | |
| progress_interval | No | Seconds between progress updates when stream=True |
Execute code directly in a kernel (not saved to notebook).
If `use_sandbox` selected an active sandbox, this tool executes on that
sandbox instead of a Jupyter kernel. This allows agents to switch between
kernel-backed and sandbox-backed execution using the same execute_code API.
Targets the current activated notebook's kernel by default. Pass kernel_id
to execute in a specific kernel directly — including raw kernels with no
notebook attached.
Recommended to use in following cases:
1. Execute Jupyter magic commands(e.g., `%timeit`, `%pip install xxx`)
2. Performance profiling and debugging.
3. View intermediate variable values(e.g., `print(xxx)`, `df.head()`)
4. Temporary calculations and quick tests(e.g., `np.mean(df['xxx'])`)
5. Execute Shell commands in Jupyter server(e.g., `!git xxx`)
Under no circumstances should you use this tool to:
1. Import new modules or perform variable assignments that affect subsequent Notebook execution
2. Execute dangerous code that may harm the Jupyter server or the user's data without permission
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | Code to execute (supports magic commands with %, shell commands with !) | |
| timeout | No | Maximum seconds to wait for execution (0 = use config default) | |
| kernel_id | No | Target an existing kernel by ID (e.g. a raw kernel with no notebook). If omitted, uses the current notebook's kernel. |
Insert a cell to specified position from the currently activated notebook.
| Name | Required | Description | Default |
|---|---|---|---|
| cell_type | Yes | Type of cell to insert | |
| cell_index | Yes | Target index for insertion (0-based), use -1 to append at end | |
| cell_source | Yes | Source content for the cell |
| Name | Required | Description |
|---|---|---|
| result | Yes | Success message and the structure of its surrounding cells |
Insert a cell at specified index from the currently activated notebook and then execute it with timeout and return it's outputs It is a shortcut tool for insert_cell and execute_cell tools, recommended to use if you want to insert a cell and execute it at the same time
| Name | Required | Description | Default |
|---|---|---|---|
| timeout | No | Maximum seconds to wait for execution (0 = use config default) | |
| cell_index | Yes | Index of the cell to insert and execute (0-based) | |
| cell_source | Yes | Code source for the cell |
List all files and directories recursively in the Jupyter server's file system. Used to explore the file system structure of the Jupyter server or to find specific files or directories.
| Name | Required | Description | Default |
|---|---|---|---|
| path | No | The starting path to list from (empty string means root directory) | |
| limit | No | Maximum number of items to return (0 means no limit) | |
| pattern | No | Glob pattern to filter file paths | |
| max_depth | No | Maximum depth to recurse into subdirectories | |
| start_index | No | Starting index for pagination (0-based) |
| Name | Required | Description |
|---|---|---|
| result | Yes | Tab-separated table with columns: Path, Type, Size, Last_Modified. Includes pagination info header. |
List all available kernels in the Jupyter server.
This tool shows all running and available kernel sessions on the Jupyter server, including their IDs, names, states, connection information, and kernel specifications. Useful for monitoring kernel resources and identifying specific kernels for connection.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes | Tab-separated table with columns: ID, Name, Display_Name, Language, State, Connections, Last_Activity, Environment |
List all notebooks that have been used via use_notebook tool
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes | TSV formatted table with notebook information |
Move a cell from source_index to target_index within the currently activated notebook.
The cell is removed from source_index and placed at target_index. Cells in between shift
to fill the gap. The cell's type, source, and outputs are preserved.
Example: in a notebook [A, B, C, D], move_cell(1, 3) produces [A, C, D, B].
Use this tool instead of manually deleting and re-inserting a cell — it is atomic and
preserves cell metadata. Use read_notebook first to see cell indices if needed.| Name | Required | Description | Default |
|---|---|---|---|
| source_index | Yes | Index of the cell to move (0-based) | |
| target_index | Yes | Destination index where the cell will end up (0-based) |
| Name | Required | Description |
|---|---|---|
| result | Yes | Success message with moved cell info and surrounding context |
Replace the entire source of a cell in the currently activated notebook. Returns a diff showing the changes made.
Use this when rewriting a cell completely. For small, targeted changes,
prefer edit_cell_source instead — it is safer for partial edits.| Name | Required | Description | Default |
|---|---|---|---|
| cell_index | Yes | Index of the cell to overwrite (0-based) | |
| cell_source | Yes | New complete cell source |
| Name | Required | Description |
|---|---|---|
| result | Yes | Success message with diff showing changes made |
Read a specific cell from the currently activated notebook and return it's metadata (index, type, execution count), source and outputs (for code cells)
| Name | Required | Description | Default |
|---|---|---|---|
| cell_index | Yes | Index of the cell to read (0-based) | |
| include_outputs | No | Include outputs in the response (only for code cells) |
Read a notebook and return index, source content, type, execution count of each cell.
Using brief format to get a quick overview of the notebook structure and it's useful for locating specific cells for operations like delete or insert. Using detailed format to get detailed information of the notebook and it's useful for debugging and analysis.
It is recommended to use brief format with larger limit to get a overview of the notebook structure, then use detailed format with exact index and limit to get the detailed information of some specific cells.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of items to return (0 means no limit) | |
| start_index | No | Starting index for pagination (0-based) | |
| notebook_name | Yes | Notebook identifier to read | |
| response_format | No | Response format: 'brief' will return first line and lines number, 'detailed' will return full cell source | brief |
| Name | Required | Description |
|---|---|---|
| result | Yes | Notebook content in the requested format |
Restart the kernel for a specific notebook.
| Name | Required | Description | Default |
|---|---|---|---|
| notebook_name | Yes | Notebook identifier to restart |
| Name | Required | Description |
|---|---|---|
| result | Yes | Success message |
Unuse from a specific notebook and release its resources.
| Name | Required | Description | Default |
|---|---|---|---|
| notebook_name | Yes | Notebook identifier to disconnect |
| Name | Required | Description |
|---|---|---|
| result | Yes | Success message |
Use a notebook and activate it for following cell operations. All cell operations will be performed on the currently activated notebook. Activate new notebook will deactivate the previously activated notebook. Reactivate previously activated notebook using same notebook_name and notebook_path.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | Notebook operation mode: 'connect' to connect to existing and activate it, 'create' to create new and activate it | connect |
| kernel_id | No | Specific kernel ID to use (will create new if skipped) | |
| notebook_name | Yes | Unique identifier for the notebook | |
| notebook_path | Yes | Path to the notebook file, relative to the Jupyter server root (e.g. 'notebook.ipynb') |
| Name | Required | Description |
|---|---|---|
| result | Yes | Success message with notebook information |
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.7insert_execute_code_cellread_notebookuse_notebookv1.0.6clear_cell_outputexecute_code2 fields changedInput schema / properties / timeout / descriptionPrevious value: -"Execution timeout in seconds"New value: +"Maximum seconds to wait for execution (0 = use config default)"Input schema / properties / timeout / maximumRemoved value: -3600insert_execute_code_celllist_filesmove_celloverwrite_cell_sourcerestart_notebookunuse_notebookv1.0.5list_filesmove_celloverwrite_cell_sourceread_notebookrestart_notebookunuse_notebookuse_notebookv1.0.4connect_to_jupyteredit_cell_sourceexecute_cell3 fields changedInput schema / properties / timeout / defaultPrevious value: -90New value: +0Input schema / properties / timeout / descriptionPrevious value: -"Maximum seconds to wait for execution"New value: +"Maximum seconds to wait for execution (0 = use config default)"Output schema / (root)Previous value: -{
- "$defs": {
- "Annotations": {
- "additionalProperties": true,
- "properties": {
- "audience": {
- "anyOf": [
- {
- "items": {
- "enum": [
- "user",
- "assistant"
- ],
- "type": "string"
- },
- "type": "array"
- },
- {
- "type": "null"
- }
- ],
- "default": null,
- "title": "Audience"
- },
- "priority": {
- "anyOf": [
- {
- "maximum": 1,
- "minimum": 0,
- "type": "number"
- },
- {
- "type": "null"
- }
- ],
- "default": null,
- "title": "Priority"
- }
- },
- "title": "Annotations",
- "type": "object"
- },
- "ImageContent": {
- "additionalProperties": true,
- "description": "Image content for a message.",
- "properties": {
- "_meta": {
- "anyOf": [
- {
- "additionalProperties": true,
- "type": "object"
- },
- {
- "type": "null"
- }
- ],
- "default": null,
- "title": "Meta"
- },
- "annotations": {
- "anyOf": [
- {
- "$ref": "#/$defs/Annotations"
- },
- {
- "type": "null"
- }
- ],
- "default": null
- },
- "data": {
- "title": "Data",
- "type": "string"
- },
- "mimeType": {
- "title": "Mimetype",
- "type": "string"
- },
- "type": {
- "const": "image",
- "title": "Type",
- "type": "string"
- }
- },
- "required": [
- "type",
- "data",
- "mimeType"
- ],
- "title": "ImageContent",
- "type": "object"
- }
- },
- "properties": {
- "result": {
- "description": "List of outputs from the executed cell",
- "items": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "$ref": "#/$defs/ImageContent"
- }
- ]
- },
- "title": "Result",
- "type": "array"
- }
- },
- "required": [
- "result"
- ],
- "title": "execute_cellOutput",
- "type": "object"
-}New value: +nullexecute_code3 fields changedInput schema / properties / kernel_idAdded value: +{
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null,
+ "description": "Target an existing kernel by ID (e.g. a raw kernel with no notebook). If omitted, uses the current notebook's kernel.",
+ "title": "Kernel Id"
+}Input schema / properties / timeout / maximumPrevious value: -60New value: +3600Output schema / (root)Previous value: -{
- "$defs": {
- "Annotations": {
- "additionalProperties": true,
- "properties": {
- "audience": {
- "anyOf": [
- {
- "items": {
- "enum": [
- "user",
- "assistant"
- ],
- "type": "string"
- },
- "type": "array"
- },
- {
- "type": "null"
- }
- ],
- "default": null,
- "title": "Audience"
- },
- "priority": {
- "anyOf": [
- {
- "maximum": 1,
- "minimum": 0,
- "type": "number"
- },
- {
- "type": "null"
- }
- ],
- "default": null,
- "title": "Priority"
- }
- },
- "title": "Annotations",
- "type": "object"
- },
- "ImageContent": {
- "additionalProperties": true,
- "description": "Image content for a message.",
- "properties": {
- "_meta": {
- "anyOf": [
- {
- "additionalProperties": true,
- "type": "object"
- },
- {
- "type": "null"
- }
- ],
- "default": null,
- "title": "Meta"
- },
- "annotations": {
- "anyOf": [
- {
- "$ref": "#/$defs/Annotations"
- },
- {
- "type": "null"
- }
- ],
- "default": null
- },
- "data": {
- "title": "Data",
- "type": "string"
- },
- "mimeType": {
- "title": "Mimetype",
- "type": "string"
- },
- "type": {
- "const": "image",
- "title": "Type",
- "type": "string"
- }
- },
- "required": [
- "type",
- "data",
- "mimeType"
- ],
- "title": "ImageContent",
- "type": "object"
- }
- },
- "properties": {
- "result": {
- "description": "List of outputs from the executed code",
- "items": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "$ref": "#/$defs/ImageContent"
- }
- ]
- },
- "title": "Result",
- "type": "array"
- }
- },
- "required": [
- "result"
- ],
- "title": "execute_codeOutput",
- "type": "object"
-}New value: +nullinsert_execute_code_cell3 fields changedInput schema / properties / timeout / defaultPrevious value: -90New value: +0Input schema / properties / timeout / descriptionPrevious value: -"Maximum seconds to wait for execution"New value: +"Maximum seconds to wait for execution (0 = use config default)"Output schema / (root)Previous value: -{
- "$defs": {
- "Annotations": {
- "additionalProperties": true,
- "properties": {
- "audience": {
- "anyOf": [
- {
- "items": {
- "enum": [
- "user",
- "assistant"
- ],
- "type": "string"
- },
- "type": "array"
- },
- {
- "type": "null"
- }
- ],
- "default": null,
- "title": "Audience"
- },
- "priority": {
- "anyOf": [
- {
- "maximum": 1,
- "minimum": 0,
- "type": "number"
- },
- {
- "type": "null"
- }
- ],
- "default": null,
- "title": "Priority"
- }
- },
- "title": "Annotations",
- "type": "object"
- },
- "ImageContent": {
- "additionalProperties": true,
- "description": "Image content for a message.",
- "properties": {
- "_meta": {
- "anyOf": [
- {
- "additionalProperties": true,
- "type": "object"
- },
- {
- "type": "null"
- }
- ],
- "default": null,
- "title": "Meta"
- },
- "annotations": {
- "anyOf": [
- {
- "$ref": "#/$defs/Annotations"
- },
- {
- "type": "null"
- }
- ],
- "default": null
- },
- "data": {
- "title": "Data",
- "type": "string"
- },
- "mimeType": {
- "title": "Mimetype",
- "type": "string"
- },
- "type": {
- "const": "image",
- "title": "Type",
- "type": "string"
- }
- },
- "required": [
- "type",
- "data",
- "mimeType"
- ],
- "title": "ImageContent",
- "type": "object"
- }
- },
- "properties": {
- "result": {
- "description": "List of outputs from the executed cell",
- "items": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "$ref": "#/$defs/ImageContent"
- }
- ]
- },
- "title": "Result",
- "type": "array"
- }
- },
- "required": [
- "result"
- ],
- "title": "insert_execute_code_cellOutput",
- "type": "object"
-}New value: +nullmove_cellread_cell1 field changedOutput schema / (root)Previous value: -{
- "$defs": {
- "Annotations": {
- "additionalProperties": true,
- "properties": {
- "audience": {
- "anyOf": [
- {
- "items": {
- "enum": [
- "user",
- "assistant"
- ],
- "type": "string"
- },
- "type": "array"
- },
- {
- "type": "null"
- }
- ],
- "default": null,
- "title": "Audience"
- },
- "priority": {
- "anyOf": [
- {
- "maximum": 1,
- "minimum": 0,
- "type": "number"
- },
- {
- "type": "null"
- }
- ],
- "default": null,
- "title": "Priority"
- }
- },
- "title": "Annotations",
- "type": "object"
- },
- "ImageContent": {
- "additionalProperties": true,
- "description": "Image content for a message.",
- "properties": {
- "_meta": {
- "anyOf": [
- {
- "additionalProperties": true,
- "type": "object"
- },
- {
- "type": "null"
- }
- ],
- "default": null,
- "title": "Meta"
- },
- "annotations": {
- "anyOf": [
- {
- "$ref": "#/$defs/Annotations"
- },
- {
- "type": "null"
- }
- ],
- "default": null
- },
- "data": {
- "title": "Data",
- "type": "string"
- },
- "mimeType": {
- "title": "Mimetype",
- "type": "string"
- },
- "type": {
- "const": "image",
- "title": "Type",
- "type": "string"
- }
- },
- "required": [
- "type",
- "data",
- "mimeType"
- ],
- "title": "ImageContent",
- "type": "object"
- }
- },
- "properties": {
- "result": {
- "description": "Cell information including index, type, source, and outputs (for code cells)",
- "items": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "$ref": "#/$defs/ImageContent"
- }
- ]
- },
- "title": "Result",
- "type": "array"
- }
- },
- "required": [
- "result"
- ],
- "title": "read_cellOutput",
- "type": "object"
-}New value: +nullv1.0.0delete_cellexecute_cellexecute_codeinsert_cellinsert_execute_code_celllist_fileslist_kernelslist_notebooksoverwrite_cell_sourceread_cellread_notebookrestart_notebookunuse_notebookuse_notebookScored across 18 tools
Most tools have clear, distinct purposes, especially with explicit guidance distinguishing edit_cell_source from overwrite_cell_source. The three execution-related tools (execute_cell, insert_execute_code_cell, execute_code) could be confused at first glance, but their descriptions clarify the differences.
The set largely follows a verb_noun pattern (read_cell, delete_cell, move_cell, restart_notebook). Minor inconsistencies like insert_execute_code_cell and unuse_notebook break the rhythm slightly, but the pattern remains predictable overall.
18 tools is on the heavier side but reasonable for a Jupyter notebook server covering connection, notebook lifecycle, cell operations, execution, and file/kernel browsing. The count is justified by the breadth of operations rather than redundancy.
The set covers notebook activation, cell reading/writing/execution/movement/deletion, kernel restarts, and dynamic connection. Missing operations like creating/saving notebooks or managing kernels beyond restarting are notable but do not severely undermine the core workflow.
Enable secure connectivity between Sentry issues and debugging data, and LLM clients, using a Model Context Protocol (MCP) server.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
Connect AI agents to Replynodes over the Model Context Protocol.
A Model Context Protocol server for Wix AI tools
