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URL: https://glama.ai/mcp/servers/integrations/etcd

⇱ etcd | Glama

  • Why this server?

    Supports etcd as a backend for Terraform state management in high-availability configurations.

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    Enables AI assistants to manage homelab infrastructure through automated service installation (Jellyfin, Pi-hole, Ollama, Home Assistant, Frigate NVR), VM operations, AI accelerator support (MemryX, Coral TPU, Hailo-8), and Terraform state management with SSH-based discovery and deployment.
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    4
    MIT

  • Why this server?

    Provides tools for analyzing etcd logs to assist SREs in monitoring core Kubernetes infrastructure health and troubleshooting cluster state issues.

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    About AI/ML-powered diagnostic engine for SRE Observability on Konflux and OpenShift. It uses the Model Context Protocol (MCP) and 40+ tools to analyze logs, metrics, and traces, enabling automated RCA and predictive analysis.
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    Apache 2.0

  • Why this server?

    Queries etcd cluster state: list members and check status through the Talos API.

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    MCP server for Talos Linux cluster management via the native gRPC API
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    MIT

  • Why this server?

    Provides diagnostic tools and expert manuals for troubleshooting etcd issues, including auto-discovery via cluster bootstrap adapter.

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    A toolkit of 256 MCP servers for SRE incident diagnosis with Claude. One agent per tech (Postgres, Kafka, Istio, Kubernetes, Prometheus, MongoDB, Redis, Cassandra, ...) with failure modes, key metrics, and runbooks baked in. Plus telemetry MCPs (PromQL/LogQL/Elasticsearch), SSH-via-bastion executor, and 33 discovery adapters across 9 clouds. Apache 2.0, runs locally. Reproducible 5/5 scenari
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    Apache 2.0

  • Why this server?

    Provides tools for managing etcd members, snapshots, alarms, and defragmentation on Talos clusters.

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    MCP server that enables interaction with Talos Linux clusters via gRPC API, supporting cluster management, monitoring, configuration, and resource inspection.
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    MIT

  • Why this server?

    Provides tools for analyzing etcd cluster performance, including metrics on WAL fsync, backend commit, disk I/O, and network I/O, with automated bottleneck detection and performance reports.

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    A comprehensive, AI-powered performance analysis and monitoring platform for OpenShift/Kubernetes clusters. This project provides Model Context Protocol (MCP) servers for analyzing etcd, network, and OVN-Kubernetes components with deep performance insights, automated root cause analysis, and actionable recommendations.
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  • Why this server?

    Provides tools for analyzing etcd logs to investigate cluster health and issues.

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    An open source MCP server empowering SREs with intelligent observability, predictive analytics, and AI-driven automation across Kubernetes, OpenShift, and Tekton environments.
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    8
    Apache 2.0