jupyter-mcp-server
Decision gist · record as of 2026-08-14
Yes, if you use Jupyter with AI agents or need to expose notebook execution via MCP. The package is actively maintained, permissively licensed, has low install friction, and solves a specific gap: real-time agent control of notebooks. Install it locally to connect your Jupyter server, or use the hosted endpoint for immediate integration. No known vulnerabilities as of 2026-08-14.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10 and a running Jupyter server instance to connect to; MCP client needed to interact with the server.
- Low install friction with a pure-Python wheel.
- Active maintenance (last commit 2026-08-14, 1244 stars) and recent release cycle.
License · maintenance · safety
permissive license (permissive) — BSD 3-Clause License (permissive) allows commercial use, modification, and redistribution with minimal restrictions—only requiring copyright notice and disclaimer retention.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 1,244 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 92,586 downloads/mo, #13,441 on PyPI
Alternatives
Verify before relying
pip install jupyter-mcp-server
from jupyter_mcp_server import JupyterMCPServer
import asyncio
server = JupyterMCPServer()
await server.connect_to_jupyter()- Whether the package can run standalone or only as a Jupyter extension or external server process.
- Performance characteristics under concurrent notebook and sandbox operations.
- Compatibility matrix with specific Jupyter Server and MCP client versions.
- Whether GPU sandbox variants require additional credentials or setup beyond the package itself.
What it is and what it does
Jupyter MCP Server is a Model Context Protocol server that bridges Jupyter notebooks and AI agents, allowing tools to read, write, and execute code in notebooks in real-time. It exposes MCP tools for notebook management (switching between notebooks, listing kernels), cell operations (reading and executing cells, managing outputs), and code sandbox execution across multiple backends including local Jupyter, Datalayer, Kaggle, Google Colab, and Modal.
The package is built on fastapi and jupyter-server, with observability via opentelemetry-api and opentelemetry-sdk. It can run as a standalone MCP server, as a Jupyter extension, or connect to existing Jupyter deployments. Datalayer hosts a public instance for immediate use without local setup. The primary use case is enabling AI agents to work with notebooks persistently—executing code, handling failures, and switching execution backends on demand.
Use it for
- Enable AI agents to execute code in local or cloud Jupyter notebooks with real-time feedback and error recovery.
- Route code execution across multiple sandbox backends without changing client code.
- Build multi-notebook workflows where an agent switches between notebooks and manages kernel state automatically.
- Integrate Jupyter execution into an MCP-compatible agent framework for persistent, context-aware data analysis.
- Use the hosted Datalayer MCP endpoint to connect any MCP client to notebooks without running a local server.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you use Jupyter with AI agents or need to expose notebook execution via MCP.
The package is actively maintained, permissively licensed, has low install friction, and solves a specific gap: real-time agent control of notebooks. Install it locally to connect your Jupyter server, or use the hosted endpoint for immediate integration. No known vulnerabilities as of 2026-08-14.
Install
jupyter-mcp-server on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance (last commit 2026-08-14, 1244 stars) and recent release cycle. Requires Python >=3.10 and 15 runtime dependencies including fastapi, jupyter-server, mcp, and opentelemetry libraries—a substantial but standard stack for Jupyter tooling.
Requires Python >=3.10 and a running Jupyter server instance to connect to; MCP client needed to interact with the server.
License in practice
BSD 3-Clause License (permissive) allows commercial use, modification, and redistribution with minimal restrictions—only requiring copyright notice and disclaimer retention.
Quickstart
pip install jupyter-mcp-server
from jupyter_mcp_server import JupyterMCPServer
import asyncio
server = JupyterMCPServer()
await server.connect_to_jupyter()Verify before relying
- Whether the package can run standalone or only as a Jupyter extension or external server process.
- Performance characteristics under concurrent notebook and sandbox operations.
- Compatibility matrix with specific Jupyter Server and MCP client versions.
- Whether GPU sandbox variants require additional credentials or setup beyond the package itself.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 15 packagescode-sandboxesdatalayer-reactorfastapijupyter-collaborationjupyter-nbmodel-clientjupyter-server-clientjupyter-server-nbmodeljupyter-servermcpopentelemetry-apiopentelemetry-sdkpycrdtpydantictyperuvicorn |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 92,586 / month, #13,441 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: System AdministratorsLicense :: OSI Approved :: BSD LicenseProgramming Language :: PythonProgramming Language :: Python :: 3 |
Evidence: jupyter_mcp_server-1.3.8-py3-none-any.whl
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See also agent-sandbox · jupyter-ai-tools · jupyter-mcp-tools · jupyter-ai · jupyter-ai-magics · papermill · databricks-mcp · jupyter-nbmodel-client · notebooklm-mcp-cli · jupyterlab-commands-toolkit