mcp-server-glassflow 0.1.0

MCP server for managing and diagnosing GlassFlow streaming pipelines

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

Tool	Description
list_pipelines	List all pipelines with status
get_pipeline	Get full V3 pipeline configuration
get_pipeline_health	Get pipeline health and status
create_pipeline	Create a new pipeline (V3 JSON config)
edit_pipeline	Edit a stopped pipeline
stop_pipeline	Stop a running pipeline
resume_pipeline	Resume a stopped pipeline
delete_pipeline	Delete a pipeline

Diagnostics

Tool	Description
diagnose_pipeline	Complete diagnostic snapshot (health + metrics + DLQ + errors)
query_pipeline_metrics	Query specific metrics (throughput, latency, DLQ rate, bytes)
query_custom_metric	Custom PromQL query (restricted to glassflow_gfm_* metrics)
query_pipeline_logs	Search logs by pipeline, severity, and component
get_pipeline_errors	Recent ERROR/WARN logs for a pipeline
get_dlq_state	Dead-letter queue message count

Resources

URI	Description
glassflow://docs/pipeline-v3-format	Complete V3 pipeline configuration reference with examples

Configuration

All configuration is via environment variables:

Variable	Default	Description
GLASSFLOW_API_URL	http://glassflow-api....:8081	GlassFlow REST API URL
VICTORIAMETRICS_URL	http://victoria-metrics....:8428	VictoriaMetrics URL for metrics queries
VICTORIALOGS_URL	http://victoria-logs....:9428	VictoriaLogs URL for log queries
MCP_PORT	8080	Port the SSE server listens on

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