af-jupyterlab-mcp
Allows users to create, inspect, list, and delete their own per-user JupyterLab servers, and query GPU availability and supported images.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@af-jupyterlab-mcpCreate a Jupyter server with 2 CPUs and a GPU"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
af-jupyterlab-mcp
MCP server that lets AF users create, inspect, and delete their own per-user JupyterLab servers on the UChicago ATLAS Analysis Facility Kubernetes cluster — the same notebooks af-portal deploys today, exposed as tools for LLMs.
Architecture
LLM <--MCP/HTTP--> af-jupyterlab-mcp <--k8s API--> notebook namespace (Pod/Service/Secret/Ingress)
^
| Authorization: Bearer <broker-issued JWT>
|
af-mcp-platform credential brokerPhase 1 (this repo, today) ships six tools that manage the Pod/Service/
Secret/Ingress quadruple for a notebook, ported from af-portal's
portal/jupyterlab.py and its four Jinja templates. Phase 2 (tracked, not yet
built) adds a typed proxy to the Datalayer jupyter-mcp-server running inside
the notebook itself — see
maniaclab/af-mcp-platform#189.
Related MCP server: rucio-mcp
Project layout
src/af_jupyterlab_mcp/
├── cli.py # argparse: `af-jupyterlab-mcp serve` (HTTP only)
├── config.py # env-driven Settings: namespace, domain, image allowlist, quotas
├── server.py # FastMCP setup, lifespan (k8s client + broker verifier), tool registration
├── auth/
│ └── broker.py # extract_bearer(), get_broker_claims() -- broker-issued JWT verification
├── k8s/
│ ├── errors.py # GuardrailError, NameConflictError, NotFoundOrNotYoursError, ...
│ ├── guardrails.py # CPU/memory/duration range + image allowlist validation
│ ├── names.py # sanitize_k8s_pod_name, name availability, name generation
│ ├── templates.py # Jinja rendering of the four ported manifests
│ ├── notebooks.py # create/get/list/delete notebook (ported portal logic)
│ ├── gpu.py # get_gpu_availability (ported portal logic)
│ └── templates/ # pod.yaml.j2, service.yaml.j2, secret.yaml.j2, ingress.yaml.j2
│ # (ported verbatim from af-portal/portal/templates/jupyterlab/)
└── tools/
└── jupyterlab.py # the six @mcp.tool() functionsTool surface
create_jupyter_serverlist_jupyter_serversget_jupyter_serverdelete_jupyter_serverget_gpu_availabilitylist_supported_images
The owner of every server is always claims.unixname from the verified broker
JWT — no tool takes an owner/username argument.
Build and test commands
pixi run test # quick tests
pixi run lint # pre-commit + pylint
pixi run helm-lint # lint + smoke-render the Helm chartThis server cannot be deployed
Maintenance
Related MCP Connectors
Massed Compute MCP — GPU inventory, VM lifecycle, billing, SSH keys, and setup recipes.
GPU cloud platform — create, manage, and monitor instances, snapshots, SSH keys, and billing.
Read GPU instances, types, images, filesystems and firewall rules; launch and terminate instances.
On-demand GPU nodes for agents: create nodes, run commands, and submit jobs, billed by the minute.
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