Cloud DevOps MCP Server
Related Servers
Alternatives to Cloud DevOps MCP Server
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityCmaintenanceProvides Kubernetes deployment intelligence with typed query tools and AI-synthesized risk briefs, enabling users to list workloads, get detailed snapshots, query Prometheus metrics, record deployment history, and generate risk assessments before promoting to production.25 PyPIApache 2.0
- AlicenseAqualityAmaintenanceEnables engineers to estimate LLM costs, check GPU VRAM fit, audit MCP configs for security risks, and analyze network device config changes and compliance—all locally, with no account or telemetry.7MIT
- AlicenseAqualityBmaintenanceEnables analyzing Salesforce deployment logs, validating metadata manifests, assessing permission risks, and generating remediation plans through deterministic, local-only rules.4MIT
- AlicenseNot gradedqualityCmaintenanceExposes 11 agent-native QA tools over stdio so coding agents can discover a project, analyze risk, select the smallest relevant test set, generate and dry-run tests, triage failures, propose tiered heals, analyze flakes, generate quality reports, and evaluate release readiness. Planning and healing tools stay read-only or propose-only, while execution defaults to dry-run, keeping agent actions safe and evidence-backed.MIT
- AlicenseAqualityCmaintenanceEnables AI coding assistants to check whether an API key is safe to expose, audit Supabase Row Level Security, generate owner-based RLS policy templates, and run a builder-specific pre-launch security checklist. Runs locally over stdio with no network calls, telemetry, or file access.5MIT
- AlicenseNot gradedqualityAmaintenanceEnables Claude to audit AI/ML projects for production readiness through deterministic static analysis, check ML system descriptions, compare model configurations, and generate readiness reports or deployment checklists. It returns scored, evidence-based findings with explicit unknowns while never executing submitted code or accessing the network.Apache 2.0
TDQS
Scored across 12 tools
Descriptions explicitly cross-reference paired tools (e.g., review_iam_policy vs review_cloud_identity_policy, review_terraform_security vs assess_terraform_change), helping agents distinguish them. However, the set includes several adjacent 'review_' tools (Kubernetes deployment vs security, GitHub Actions vs generic CI/CD) that could still cause hesitation without careful reading.
All tool names follow a consistent snake_case verb_noun pattern using assess_, build_, estimate_, and review_ prefixes. No naming inconsistencies or mixed conventions are present.
With 12 tools, the set sits comfortably within the ideal 3–15 range. Each tool targets a distinct domain or analysis type, and no tool appears redundant.
The surface covers major DevOps analysis domains: Terraform change and security, cloud change bundles, CI/CD and GitHub Actions, IAM and cloud identity, Kubernetes deployment and security, SLO budgets, incident runbooks, and supply chain. Some gaps exist (e.g., cost optimization, compliance, or actual remediation), but the server explicitly scopes itself as analysis-only, so coverage is strong for that purpose.