Luviner
Connect your agent to Luviner’s remote MCP for time-series anomaly detection, classification, and root-cause hints on production metrics.
Overview
Luviner MCP is a MCP server for the Operate phase that provides 13 neural engines for time-series anomaly detection, classification, and root cause analysis over remote HTTP.
What is this MCP server?
- 13 neural engines for time-series workloads
- Anomaly detection, classification, and root cause analysis
- Remote streamable-http endpoint at mcp.luviner.com/mcp
- Bearer token auth from luviner.com/api-access
- MCP registry version 1.0.0 with no local npm stdio package
- 13 neural engines advertised in server description
- Remote MCP URL https://mcp.luviner.com/mcp
- Registry version 1.0.0; streamable-http with required Authorization header
What problem does it solve?
Production metrics spike without clear cause, and one-person teams lack time to manually correlate series and train custom detectors.
Who is it for?
Indie operators with live time-series telemetry who want MCP-native ML monitoring without self-hosting model stacks.
Skip if: Greenfield apps with no metrics yet, static sites with no series data, or teams that require fully on-prem-only tooling.
What do I get? / Deliverables
Your agent can call Luviner engines through MCP to flag anomalies, classify patterns, and surface root-cause leads during incidents.
- Anomaly detection results on submitted time-series
- Classification outputs across Luviner engine set
- Root cause analysis signals consumable in agent workflows
Recommended MCP Servers
Journey fit
Once you ship, metric weirdness lands in Operate; neural monitoring engines support keeping services healthy without a full data team. Monitoring matches anomaly detection and RCA on live series—not initial model training in Build.
How it compares
Remote monitoring MCP with neural time-series engines, not a local coding skill or generic log grep helper.
Common Questions / FAQ
Who is io.github.filippogroppi/luviner for?
Solo builders and small teams running production APIs or SaaS who monitor time-series metrics and want agent-assisted anomaly and RCA analysis.
When should I use io.github.filippogroppi/luviner?
Use it during operate/monitoring when incidents or drift show up in metrics and you need structured detection and classification via MCP.
How do I add io.github.filippogroppi/luviner to my agent?
Register the remote MCP URL https://mcp.luviner.com/mcp with streamable-http transport and an Authorization Bearer token from luviner.com/api-access in your client’s MCP settings.