GlassFlow MCP Server

Model Context Protocol server for managing and diagnosing GlassFlow streaming pipelines. Exposes pipeline CRUD, metrics queries, log search, and a composite diagnostic tool as MCP tools that AI agents (Claude Code, etc.) can call over SSE transport.

Features

• Pipeline management — create, list, get, edit, stop, resume, delete pipelines
• Diagnostics — query throughput, latency, DLQ state, and error logs
• diagnose_pipeline — single-call diagnostic snapshot combining health, metrics, DLQ, and recent errors
• V3 config reference — MCP resource with the complete pipeline configuration format
• Uses the official GlassFlow Python SDK

Quick start

Local development

pip install -e .

export GLASSFLOW_API_URL="http://localhost:8081"  # or port-forward to your cluster
python -m glassflow_mcp.server

Connect Claude Code

claude mcp add --transport sse glassflow http://localhost:8080/sse

Start a new Claude Code session — the GlassFlow tools will appear automatically.

Available tools

Pipeline management

Diagnostics

Resources

Configuration

All configuration is via environment variables:

Deployment

Docker

docker build -t glassflow-mcp-server .
docker run -p 8080:8080 \
  -e GLASSFLOW_API_URL=http://your-glassflow-api:8081 \
  glassflow-mcp-server

Kubernetes

Example manifests are provided in k8s/examples/. Copy them, edit the CHANGEME values, and apply:

kubectl apply -f k8s/examples/deployment.yaml -f k8s/examples/service.yaml

Then connect via port-forward:

kubectl port-forward -n <namespace> svc/glassflow-mcp 8080:8080
claude mcp add --transport sse glassflow http://localhost:8080/sse

See k8s/README.md for full details including optional Ingress setup.

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest -v

# Lint
ruff check src/ tests/
ruff format --check src/ tests/

License

MIT