{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-datalayer-jupyter-mcp-server","slug":"mcp-datalayer-jupyter-mcp-server","name":"jupyter-mcp-server","type":"mcp","url":"https://github.com/datalayer/jupyter-mcp-server","page_url":"https://unfragile.ai/mcp-datalayer-jupyter-mcp-server","categories":["mcp-servers"],"tags":["ai","jupyter","mcp","mcp-server","tools"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-datalayer-jupyter-mcp-server__cap_0","uri":"capability://tool.use.integration.mcp.protocol.bridging.to.jupyter.environments","name":"mcp protocol bridging to jupyter environments","description":"Implements a FastMCP-based server that translates Model Context Protocol messages from AI clients (Claude Desktop, VS Code, Cursor) into Jupyter API calls, using STDIO and HTTP transports with CORS middleware. The server maintains a singleton ServerContext for configuration and routes requests through a tool registry to 15+ specialized notebook operation tools, enabling stateful interaction with Jupyter kernels and notebook documents.","intents":["Connect Claude Desktop or VS Code to a running Jupyter instance for AI-assisted notebook editing","Enable an AI agent to read, modify, and execute notebook cells programmatically","Expose Jupyter capabilities to MCP-compatible clients without modifying the notebook interface"],"best_for":["AI developers building Claude Desktop or VS Code extensions that need Jupyter integration","Teams deploying AI agents that must interact with Jupyter notebooks as a data science tool","Organizations running JupyterHub or JupyterLab who want to expose notebooks to MCP clients"],"limitations":["Requires a running Jupyter server instance — cannot operate on offline notebooks","STDIO transport adds latency for high-frequency tool calls due to process serialization overhead","Multi-notebook session management requires explicit notebook ID tracking; no automatic context switching"],"requires":["Python 3.9+","Jupyter Server 2.0+ or JupyterLab 3.0+","MCP-compatible client (Claude Desktop, VS Code with MCP extension, Cursor, Cline, Windsurf)","Network connectivity between MCP client and Jupyter server (localhost or remote)"],"input_types":["MCP protocol messages (JSON-RPC 2.0)","Tool invocation requests with parameters","Notebook identifiers and cell references"],"output_types":["MCP protocol responses (JSON-RPC 2.0)","Notebook metadata and cell contents","Execution results with multimodal outputs (text, images, plots)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-datalayer-jupyter-mcp-server__cap_1","uri":"capability://automation.workflow.multi.notebook.session.management.with.concurrent.kernel.execution","name":"multi-notebook session management with concurrent kernel execution","description":"The NotebookManager component maintains isolated session state for multiple notebooks, tracking kernel connections, cell execution order, and output buffers per notebook. It implements session lifecycle management (open, close, switch) and routes execution requests to the correct kernel instance, enabling AI clients to work with multiple notebooks in parallel without cross-contamination of kernel state or variable scope.","intents":["Execute code in one notebook while reading from another without mixing kernel state","Manage multiple long-running analyses simultaneously, each with independent variable scopes","Switch between notebooks in a multi-notebook project without losing execution history"],"best_for":["Data scientists running parallel experiments across multiple notebooks","AI agents orchestrating multi-step workflows that span multiple notebook files","Teams collaborating on projects with notebook-per-component architecture"],"limitations":["Session state is ephemeral — restarting the MCP server loses all notebook session references","No built-in persistence of execution history across server restarts","Kernel memory is shared within a notebook session; no automatic isolation between cells","Concurrent execution across multiple notebooks requires explicit synchronization at the client level"],"requires":["Multiple notebook files accessible to the Jupyter server","Separate kernel instances per notebook (default Jupyter behavior)","Client-side tracking of notebook IDs for session management"],"input_types":["Notebook file paths or identifiers","Cell execution requests with notebook context","Session lifecycle commands (open, close, list)"],"output_types":["Session metadata (notebook ID, kernel status, execution count)","Cell execution results scoped to specific notebooks","List of active notebook sessions"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-datalayer-jupyter-mcp-server__cap_10","uri":"capability://automation.workflow.docker.containerization.with.multi.architecture.support","name":"docker containerization with multi-architecture support","description":"Distributes the MCP server as a multi-architecture Docker image (datalayer/jupyter-mcp-server) supporting amd64 and arm64 platforms. The Dockerfile installs the jupyter-mcp-server package and Jupyter dependencies, enabling one-command deployment in containerized environments. The image includes both standalone server and extension modes, selectable via environment variables or command-line arguments.","intents":["Deploy the MCP server in Kubernetes or Docker Compose without manual dependency installation","Run the server on ARM-based infrastructure (Apple Silicon, Raspberry Pi) without building from source","Integrate the MCP server into existing containerized Jupyter deployments"],"best_for":["Kubernetes deployments where the MCP server runs as a sidecar or separate pod","Docker Compose setups that bundle Jupyter and MCP server together","CI/CD pipelines that need reproducible MCP server environments"],"limitations":["Docker image size is ~500MB+ due to Jupyter and dependencies; slow to pull on limited bandwidth","Container networking requires explicit port exposure; default port 4040 must be mapped","No built-in health checks or graceful shutdown handling in the base image","Image is rebuilt for each release; no automatic updates for security patches"],"requires":["Docker or container runtime (Podman, containerd, etc.)","Port 4040 (or custom port) available on the host","Network connectivity to Jupyter Server (if running in separate container)"],"input_types":["Docker run command with environment variables or mount points"],"output_types":["Running MCP server process listening on port 4040"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-datalayer-jupyter-mcp-server__cap_11","uri":"capability://data.processing.analysis.multimodal.output.processing.with.image.and.plot.rendering","name":"multimodal output processing with image and plot rendering","description":"Captures and processes cell execution outputs in multiple MIME types (text/plain, text/html, image/png, image/svg+xml, application/json), converting matplotlib figures and pandas DataFrames into base64-encoded images or HTML. The output processor preserves the original MIME type metadata, allowing clients to render outputs appropriately (display images, render tables, parse JSON).","intents":["Capture matplotlib plots from cell execution and return them as images for display","Render pandas DataFrames as HTML tables that can be displayed in the client","Return structured outputs (JSON, dictionaries) that the AI can parse and reason about"],"best_for":["Data science workflows where AI needs to see plots and tables to make decisions","Interactive debugging where AI analyzes visual outputs to diagnose issues","Automated report generation where plots and tables are extracted from notebooks"],"limitations":["Output capture is limited to Jupyter's standard MIME types; custom outputs may be lost","Large outputs (>10MB images) may cause memory issues or timeout during transmission","Base64 encoding increases output size by ~33%; large plots can exceed message size limits","HTML outputs are not sanitized; malicious HTML in cell outputs could be executed by clients","Image quality depends on matplotlib DPI settings; low-DPI plots may be unreadable"],"requires":["Cell must produce output in a supported MIME type","Matplotlib or other plotting library must be installed in the kernel"],"input_types":["Cell execution request"],"output_types":["Text output (stdout, stderr)","Images as base64-encoded PNG or SVG","HTML tables and formatted output","JSON and structured data","Execution metadata (execution_count, timestamp)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-datalayer-jupyter-mcp-server__cap_12","uri":"capability://automation.workflow.configuration.management.with.environment.variable.and.file.based.settings","name":"configuration management with environment variable and file-based settings","description":"Implements ServerContext singleton that loads configuration from environment variables and optional config files, managing settings like Jupyter Server URL, authentication tokens, notebook paths, and deployment mode (standalone vs. extension). Configuration is loaded at server startup and cached in memory, allowing clients to query current settings via tools.","intents":["Configure the MCP server to connect to a remote Jupyter Server via environment variables","Set authentication tokens for secure Jupyter Server access without hardcoding credentials","Specify which notebooks are accessible to MCP clients via configuration"],"best_for":["Containerized deployments where configuration is passed via environment variables","Multi-tenant environments where different configurations are needed per deployment","Development workflows where configuration changes frequently"],"limitations":["Configuration is loaded at startup; changes require server restart to take effect","No runtime configuration updates via MCP tools; all changes must be external","Environment variables are visible in process listings; sensitive credentials may be exposed","No configuration validation at startup; invalid settings may cause runtime errors"],"requires":["Environment variables set before server startup (for containerized deployments)","Optional config file in YAML or JSON format"],"input_types":["Environment variables (e.g., JUPYTER_SERVER_URL, JUPYTER_TOKEN)","Configuration file path"],"output_types":["Parsed configuration object","Current settings queryable via tools"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-datalayer-jupyter-mcp-server__cap_13","uri":"capability://automation.workflow.error.handling.and.execution.failure.reporting.with.detailed.diagnostics","name":"error handling and execution failure reporting with detailed diagnostics","description":"Implements comprehensive error handling that captures kernel errors (syntax errors, runtime exceptions, timeouts), network errors (connection failures, timeouts), and MCP protocol errors (invalid requests, schema violations). Errors are returned to clients with detailed diagnostic information (error type, traceback, execution context) enabling AI clients to understand failures and retry intelligently.","intents":["Understand why a cell execution failed (syntax error, runtime exception, timeout)","Distinguish between transient errors (network timeout) and permanent errors (syntax error) for retry logic","Debug notebook issues by examining error tracebacks and execution context"],"best_for":["AI agents that need to handle execution failures gracefully and retry intelligently","Interactive debugging workflows where detailed error information helps diagnose issues","Automated testing pipelines that validate notebook execution and capture failures"],"limitations":["Error messages are kernel-specific; Python tracebacks differ from R or Julia errors","Timeout errors may not include partial output; long-running cells that timeout lose intermediate results","Network errors may not include detailed diagnostics; connection failures may appear as generic timeouts","Error context is limited to the failing cell; no cross-cell dependency analysis"],"requires":["Live kernel instance to capture execution errors","MCP client that can handle error responses"],"input_types":["Cell execution request"],"output_types":["Error response with error type, message, and traceback","Execution metadata (execution_count, timestamp)","Partial output if available before error"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-datalayer-jupyter-mcp-server__cap_14","uri":"capability://planning.reasoning.prompt.templates.for.notebook.specific.ai.tasks","name":"prompt templates for notebook-specific ai tasks","description":"Provides pre-built prompt templates (via MCP's prompts/list and prompts/get endpoints) that guide AI clients in common notebook tasks like code review, debugging, data exploration, and documentation generation. Templates include context about notebook structure and execution state, reducing the need for clients to construct prompts from scratch.","intents":["Get a prompt template for reviewing notebook code quality and suggesting improvements","Use a template to guide AI in debugging a failing cell by analyzing error messages and context","Generate documentation for a notebook by providing a template that extracts key insights"],"best_for":["AI clients that need structured guidance for notebook-specific tasks","Teams standardizing how AI interacts with notebooks across projects","Interactive workflows where users want AI to follow specific analysis patterns"],"limitations":["Prompt templates are static; cannot adapt to specific notebook content or user preferences","Templates are English-only; no localization for other languages","No versioning of templates; changes require server restart","Templates are hardcoded; no dynamic template generation based on notebook structure"],"requires":["MCP client that supports prompts/list and prompts/get endpoints"],"input_types":["Prompt template name"],"output_types":["Prompt template text with placeholders for notebook context","List of available templates"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-datalayer-jupyter-mcp-server__cap_2","uri":"capability://code.generation.editing.cell.level.code.reading.and.writing.with.ast.aware.insertion","name":"cell-level code reading and writing with ast-aware insertion","description":"Exposes tools for reading notebook cell contents (code, markdown, raw) and writing new cells with position control (before, after, replace). The implementation preserves notebook structure by respecting cell boundaries and execution order, allowing AI clients to inspect code context before modification and insert cells at semantically meaningful positions without corrupting the notebook document structure.","intents":["Read the current state of a notebook cell to understand what code is already there","Insert new code cells at specific positions (e.g., after a data loading cell) without disrupting execution order","Replace a cell's contents while preserving its metadata and position in the notebook"],"best_for":["AI code assistants that need to understand notebook structure before suggesting modifications","Automated notebook generation tools that must maintain semantic cell ordering","Interactive debugging workflows where AI reads cell output and modifies upstream cells"],"limitations":["No built-in AST parsing of cell contents — treats code as opaque strings, limiting semantic-aware refactoring","Cell insertion does not automatically update dependent cells' variable references","No conflict detection if multiple clients modify the same cell simultaneously","Markdown and raw cells are treated identically to code cells; no special handling for documentation"],"requires":["Notebook file must be writable by the Jupyter server process","Cell indices must be valid (0 to cell_count - 1)","Notebook format must be Jupyter's standard .ipynb JSON structure"],"input_types":["Notebook ID and cell index","Cell content (code string, markdown, raw text)","Cell type (code, markdown, raw)","Position specifier (before, after, replace)"],"output_types":["Cell contents as strings","Cell metadata (type, execution_count, outputs)","Confirmation of write operations with updated cell indices"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-datalayer-jupyter-mcp-server__cap_3","uri":"capability://code.generation.editing.synchronous.and.asynchronous.cell.execution.with.output.capture","name":"synchronous and asynchronous cell execution with output capture","description":"Executes notebook cells against a live kernel using Jupyter's execute_request message protocol, capturing stdout, stderr, and structured outputs (plots, dataframes, images) in real-time. Supports both blocking execution (wait for completion) and non-blocking execution (poll for results), with output buffering that preserves multimodal content including matplotlib figures and pandas DataFrames rendered as HTML.","intents":["Run a code cell and get back the result (stdout, errors, or structured output) synchronously","Execute long-running cells asynchronously and poll for completion without blocking the client","Capture plots, tables, and other rich outputs from cell execution for display or analysis"],"best_for":["Interactive AI debugging workflows that need immediate cell execution feedback","Batch processing agents that execute multiple cells and aggregate results","Real-time notebook execution monitoring where clients need to track progress"],"limitations":["Execution is kernel-specific — cannot execute cells from different notebooks in the same transaction","Output capture is limited to Jupyter's standard MIME types (text, image/png, text/html); custom outputs may be lost","Asynchronous execution requires client-side polling; no server-side event streaming","Execution timeout is kernel-dependent; no built-in timeout enforcement at the MCP layer","Variable scope is global to the kernel — no cell-level isolation or sandboxing"],"requires":["Live kernel instance connected to the notebook","Kernel must support execute_request (all standard Jupyter kernels do)","Cell code must be syntactically valid for the kernel language"],"input_types":["Notebook ID and cell index","Execution mode (sync or async)","Optional timeout duration"],"output_types":["Execution status (success, error, pending)","stdout and stderr as strings","Structured outputs (images as base64, HTML tables, JSON data)","Execution metadata (execution_count, timestamp)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-datalayer-jupyter-mcp-server__cap_4","uri":"capability://automation.workflow.notebook.metadata.inspection.and.kernel.status.monitoring","name":"notebook metadata inspection and kernel status monitoring","description":"Provides tools to inspect notebook metadata (kernel name, language, nbformat version), query kernel status (idle, busy, dead), and retrieve execution history (cell execution counts, timestamps). Implements polling-based kernel health monitoring that allows AI clients to determine if a kernel is ready for execution or if previous operations are still in flight.","intents":["Check if a kernel is ready before executing a cell to avoid queuing errors","Determine the notebook's language (Python, R, Julia) to validate code syntax before execution","Retrieve execution history to understand the order in which cells have been run"],"best_for":["AI agents that need to validate kernel readiness before executing code","Multi-language notebook environments where code syntax depends on kernel type","Debugging workflows that need to understand execution order and timing"],"limitations":["Kernel status is polled, not streamed — clients must implement polling loops for real-time monitoring","Execution history is limited to execution_count; no detailed timing or performance metrics","Kernel metadata is static — changes to kernel configuration require server restart to reflect","No kernel restart or interrupt capabilities exposed through this tool"],"requires":["Live kernel instance","Notebook must be open in the Jupyter server"],"input_types":["Notebook ID"],"output_types":["Kernel name (e.g., 'python3', 'ir', 'julia')","Kernel status (idle, busy, dead)","Notebook metadata (nbformat, language)","Execution history (cell execution counts)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-datalayer-jupyter-mcp-server__cap_5","uri":"capability://automation.workflow.jupyterlab.ui.integration.with.cell.focus.and.navigation","name":"jupyterlab ui integration with cell focus and navigation","description":"Implements tools that interact with JupyterLab's frontend state, including setting the active cell, scrolling to a cell, and triggering UI actions (save, run). Uses JupyterLab's message protocol to communicate with the frontend, allowing AI clients to control the notebook view and focus without requiring direct browser automation or UI scripting.","intents":["Scroll the JupyterLab interface to show a specific cell that the AI is about to modify","Set focus on a cell so the user sees what the AI is working on","Trigger notebook save or run operations from the AI client"],"best_for":["Interactive AI assistants that need to guide users' attention to relevant cells","Collaborative workflows where AI and human work on the same notebook simultaneously","Automated notebook execution where the AI needs to trigger UI-level operations"],"limitations":["Only works with JupyterLab; not compatible with classic Jupyter Notebook or other frontends","Requires JupyterLab to be open and connected — no effect if notebook is accessed via API only","UI state changes are ephemeral — closing and reopening the notebook loses focus/scroll position","No bidirectional communication with JupyterLab UI; client cannot receive user interactions"],"requires":["JupyterLab 3.0+ with WebSocket connection to the server","Notebook must be open in JupyterLab frontend"],"input_types":["Notebook ID and cell index","UI action type (focus, scroll, save, run)"],"output_types":["Confirmation of UI action completion","Current UI state (active cell, scroll position)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-datalayer-jupyter-mcp-server__cap_6","uri":"capability://data.processing.analysis.notebook.file.i.o.with.format.preservation","name":"notebook file i/o with format preservation","description":"Implements tools to read and write notebook files (.ipynb) from the filesystem, preserving the Jupyter notebook JSON structure, cell metadata, and output artifacts. Uses Jupyter's nbformat library to parse and serialize notebooks, ensuring compatibility with Jupyter's format versioning and preventing corruption of notebook files during read-write cycles.","intents":["Load a notebook file from disk to inspect its structure before execution","Save modifications to a notebook file without losing metadata or output history","Export notebook contents in formats compatible with Jupyter's ecosystem"],"best_for":["Batch notebook processing pipelines that need to read and modify multiple notebooks","CI/CD workflows that validate or transform notebooks before deployment","Notebook versioning systems that need to preserve format integrity"],"limitations":["File I/O is synchronous — large notebooks (>100MB) may block the MCP server","No atomic transactions — partial writes can corrupt notebook files if interrupted","Notebook outputs are preserved but not re-executed; stale outputs may be saved","No conflict detection for concurrent writes from multiple clients"],"requires":["Notebook file path must be accessible to the Jupyter server process","File permissions must allow read/write access","Notebook must be in valid .ipynb JSON format"],"input_types":["Notebook file path","Notebook content (JSON structure)"],"output_types":["Notebook content as parsed JSON","Notebook metadata (kernel, nbformat version)","Cell contents and outputs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-datalayer-jupyter-mcp-server__cap_7","uri":"capability://tool.use.integration.tool.discovery.and.dynamic.capability.advertisement","name":"tool discovery and dynamic capability advertisement","description":"Implements MCP's tools/list and tools/describe endpoints to dynamically advertise the 15+ available tools with their schemas, parameters, and descriptions. The tool registry is populated at server startup from the tools/ directory, allowing clients to discover capabilities without hardcoding tool names. Each tool includes JSON Schema definitions for input validation and output typing.","intents":["Allow MCP clients to discover what notebook operations are available without consulting documentation","Enable clients to validate tool parameters against JSON Schema before invocation","Support dynamic tool registration if new tools are added to the server"],"best_for":["Generic MCP clients (Claude Desktop, VS Code) that need to discover server capabilities dynamically","Tool builders extending the server with custom notebook operations","Integration tests that validate tool schemas and availability"],"limitations":["Tool schemas are static — cannot reflect runtime-dependent parameters (e.g., available notebooks)","No versioning of tool schemas; breaking changes require server restart","Tool descriptions are hardcoded strings; no dynamic documentation generation","No capability negotiation — all tools are advertised regardless of client compatibility"],"requires":["MCP client that supports tools/list and tools/describe endpoints"],"input_types":["None (discovery is client-initiated)"],"output_types":["List of tool names and descriptions","JSON Schema for each tool's input and output"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-datalayer-jupyter-mcp-server__cap_8","uri":"capability://automation.workflow.standalone.server.deployment.with.http.transport.and.cors","name":"standalone server deployment with http transport and cors","description":"Deploys the MCP server as a standalone process (default port 4040) using FastAPI with CORS middleware, enabling HTTP-based MCP clients to connect without STDIO pipes. The server implements FastMCPWithCORS, a custom FastMCP subclass that adds CORS headers for cross-origin requests, allowing web-based MCP clients and browser extensions to communicate with the server.","intents":["Run the Jupyter MCP server as a separate service that multiple clients can connect to","Enable web-based MCP clients or browser extensions to communicate with the server","Deploy the server in containerized environments (Docker, Kubernetes) without modifying Jupyter"],"best_for":["Teams running centralized Jupyter servers that multiple AI clients need to access","Docker/Kubernetes deployments where the MCP server runs as a sidecar or separate container","Web-based MCP clients that cannot use STDIO transport"],"limitations":["Standalone server requires separate process management (systemd, Docker, etc.)","HTTP transport adds network latency compared to STDIO (typically 50-200ms per request)","CORS configuration is global; cannot restrict access per-client without reverse proxy","No built-in authentication or authorization — relies on network isolation or reverse proxy","Port 4040 must be available and not blocked by firewall"],"requires":["Python 3.9+","FastAPI and uvicorn (installed as dependencies)","Network connectivity between client and server","Port 4040 (or custom port) available on the server"],"input_types":["HTTP POST requests with MCP protocol messages (JSON-RPC 2.0)"],"output_types":["HTTP responses with MCP protocol messages (JSON-RPC 2.0)","CORS headers for cross-origin requests"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-datalayer-jupyter-mcp-server__cap_9","uri":"capability://automation.workflow.jupyter.server.extension.deployment.with.native.integration","name":"jupyter server extension deployment with native integration","description":"Deploys the MCP server as a Jupyter Server extension (handlers.py), embedding it directly into the Jupyter Server process without requiring a separate service. The extension registers MCP endpoints alongside Jupyter's native API, allowing clients to connect via the same WebSocket or HTTP connection as the notebook frontend, with automatic authentication inheritance from Jupyter's session management.","intents":["Integrate the MCP server into an existing Jupyter Server without running a separate process","Leverage Jupyter's authentication and session management for MCP client access","Deploy the server in JupyterHub environments where each user has a separate Jupyter Server"],"best_for":["JupyterHub deployments where each user needs MCP access to their own Jupyter Server","Organizations that want to avoid managing a separate MCP server process","Environments where network isolation between Jupyter and MCP is required"],"limitations":["Extension must be installed in the Jupyter Server environment; cannot be deployed separately","MCP server lifecycle is tied to Jupyter Server lifecycle — cannot restart independently","Extension adds overhead to Jupyter Server process; no resource isolation","Debugging extension issues requires Jupyter Server logs; harder to troubleshoot than standalone server","Extension compatibility depends on Jupyter Server version"],"requires":["Jupyter Server 2.0+","jupyter-mcp-server package installed in the same Python environment as Jupyter Server","Jupyter Server configuration to enable the extension"],"input_types":["MCP protocol messages via Jupyter Server's WebSocket or HTTP endpoints"],"output_types":["MCP protocol responses via Jupyter Server's WebSocket or HTTP endpoints"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Jupyter Server 2.0+ or JupyterLab 3.0+","MCP-compatible client (Claude Desktop, VS Code with MCP extension, Cursor, Cline, Windsurf)","Network connectivity between MCP client and Jupyter server (localhost or remote)","Multiple notebook files accessible to the Jupyter server","Separate kernel instances per notebook (default Jupyter behavior)","Client-side tracking of notebook IDs for session management","Docker or container runtime (Podman, containerd, etc.)","Port 4040 (or custom port) available on the host","Network connectivity to Jupyter Server (if running in separate container)"],"failure_modes":["Requires a running Jupyter server instance — cannot operate on offline notebooks","STDIO transport adds latency for high-frequency tool calls due to process serialization overhead","Multi-notebook session management requires explicit notebook ID tracking; no automatic context switching","Session state is ephemeral — restarting the MCP server loses all notebook session references","No built-in persistence of execution history across server restarts","Kernel memory is shared within a notebook session; no automatic isolation between cells","Concurrent execution across multiple notebooks requires explicit synchronization at the client level","Docker image size is ~500MB+ due to Jupyter and dependencies; slow to pull on limited bandwidth","Container networking requires explicit port exposure; default port 4040 must be mapped","No built-in health checks or graceful shutdown handling in the base image","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.44182501001460645,"quality":0.35,"ecosystem":0.55,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:34.856Z","last_commit":"2026-04-27T06:07:54Z"},"community":{"stars":1059,"forks":157,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-datalayer-jupyter-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=mcp-datalayer-jupyter-mcp-server"}},"signature":"4Sj45GRnORfXXbOr9yaCP4SDevVFfiYjg6pBpAcYzdAUGaqHG6xyNug4HKkYwnCVjf0FzCf2twzsBT22uhJJBw==","signedAt":"2026-06-21T18:32:14.407Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-datalayer-jupyter-mcp-server","artifact":"https://unfragile.ai/mcp-datalayer-jupyter-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-datalayer-jupyter-mcp-server","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}