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Kubeflow MCP Server

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Kubeflow MCP Server

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Proposal: KEP-936 · ROADMAP · SECURITY · CONTRIBUTING

Overview

The Kubeflow MCP Server exposes Kubeflow Training operations as Model Context Protocol tools, enabling AI agents (Claude, Cursor, Claude Code, or any custom agents etc.) to plan, submit, monitor, and manage training jobs through natural language — without users needing to learn Kubernetes or the Kubeflow SDK directly.

Benefits

  • Agent-Native: Tools auto-discovered via MCP — no manual API wiring

  • Guided Workflow: Phase ordering with next-step hints (Plan → Discover → Train → Monitor)

  • Preview-Before-Submit: Every mutating operation requires explicit confirmation

  • Security-First: Persona gating, namespace enforcement, input validation, bearer/JWT auth

  • Multi-Platform: Auto-detects OpenShift, EKS, GKE with platform-specific guidance

  • Token-Efficient: Progressive/semantic modes compress 23 tools into 2-3 meta-tools

  • Extensible: Plugin architecture for additional Kubeflow clients (TODO: optimizer, hub)

Get Started

Install from source

git clone https://github.com/kubeflow/mcp-server.git
cd mcp-server
pip install .

Run the server

kubeflow-mcp serve

Once published to PyPI, install with pip install kubeflow-mcp.

Example: Fine-tune a model via AI agent

Once connected, your AI agent can run a complete training workflow through natural language:

User: "Fine-tune gemma-2b on the alpaca dataset"

Agent calls: check_compatibility()        → ✅ K8s 1.29, Trainer CRD installed
Agent calls: get_cluster_resources()      → 4x A100 GPUs available
Agent calls: estimate_resources("google/gemma-2b") → needs ~16GB GPU, 1x A100
Agent calls: list_runtimes()              → torchtune-llama, torchtune-gemma, ...
Agent calls: fine_tune(                   → preview config (confirmed=False)
    model="hf://google/gemma-2b",
    dataset="hf://tatsu-lab/alpaca",
    runtime="torchtune-gemma-2b"
)
Agent calls: fine_tune(..., confirmed=True) → TrainJob "train-gemma-abc" created
Agent calls: get_training_logs("train-gemma-abc") → training progress...

Every mutating tool requires confirmed=True — agents always preview before submitting.

MCP Client Config

Add to .cursor/mcp.json (or use the .mcp.json at the repo root for local dev):

{
  "mcpServers": {
    "kubeflow": {
      "command": "uv",
      "args": ["run", "kubeflow-mcp", "serve"]
    }
  }
}
claude mcp add kubeflow -- kubeflow-mcp serve

Tools

23 tools organized by workflow phase:

Phase

Tools

Description

Planning

pre_flight, check_compatibility, get_cluster_resources, estimate_resources

Environment validation and resource estimation

Discovery

list_training_jobs, get_training_job, list_runtimes, get_runtime

Browse jobs and available runtimes

Training

fine_tune, run_custom_training, run_container_training

Submit LoRA/QLoRA fine-tuning, custom scripts, or container jobs

Monitoring

get_training_logs, get_training_events, wait_for_training

Track progress, debug failures

Lifecycle

delete_training_job, update_training_job

Manage existing jobs (ownership-guarded)

Platform

inspect_crd, inspect_controller, patch_runtime, create_runtime, delete_runtime

Cluster inspection and runtime management

Health

health_check, get_server_logs

Server diagnostics

Requirements

MCP Server

Kubeflow Trainer

Kubeflow SDK

Python

Kubernetes

0.1.x

>= 2.2.0

>= 0.4.0

3.10 - 3.12

>= 1.27

CLI Reference

kubeflow-mcp serve

kubeflow-mcp serve \
  --clients trainer \             # modules: trainer, optimizer (stub), hub (stub)
  --persona ml-engineer \         # readonly | data-scientist | ml-engineer | platform-admin
  --mode full \                   # full | progressive | semantic
  --instruction-tier full \       # full | compact | minimal
  --transport stdio \             # stdio | http | sse
  --auth-token SECRET \           # bearer token for HTTP auth (dev/staging)
  --log-level INFO \              # DEBUG | INFO | WARNING | ERROR
  --log-format console \          # console | json (auto-detected if omitted)
  --no-banner                     # suppress startup banner

--mode progressive exposes 3 meta-tools (~85 tokens) for hierarchical discovery. --mode semantic exposes 2 meta-tools (~69 tokens) using embedding search. Both reduce token consumption significantly for agent workflows.

When using --transport http, configure auth to secure the endpoint:

# Simple API key (dev/staging)
kubeflow-mcp serve --transport http --auth-token my-secret-token

# Or via env var
export KUBEFLOW_MCP_AUTH_TOKEN=my-secret-token
kubeflow-mcp serve --transport http

# JWT verification (production)
export KUBEFLOW_MCP_JWKS_URI=https://auth.example.com/.well-known/jwks.json
export KUBEFLOW_MCP_JWT_ISSUER=https://auth.example.com
export KUBEFLOW_MCP_JWT_AUDIENCE=kubeflow-mcp
kubeflow-mcp serve --transport http

Without auth configured, the server logs a warning that the HTTP endpoint is open.

kubeflow-mcp agent \
  --backend ollama \              # ollama (default; more backends planned)
  --model qwen3:8b \              # model name for the backend
  --mode full \                   # full | progressive | semantic
  --thinking                      # enable thinking output (supported models)

Development

make install-dev                  # setup environment
make verify                       # lint + format check
make test-python                  # run tests
make inspector                    # launch MCP Inspector (stdio)
make inspector TRANSPORT=http     # Inspector + Streamable HTTP (start server separately)
make inspector TRANSPORT=sse      # Inspector + SSE (start server separately)

Community

Documentation

License

Apache License 2.0 — see LICENSE.

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