Cloud DevOps MCP Server
Related Servers
Alternatives to Cloud DevOps MCP Server
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityDmaintenanceProvides 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.Apache 2.0
- AlicenseAqualityBmaintenanceEnables 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
- AlicenseNot gradedqualityBmaintenanceEnables AI assistants to audit git diffs, scan for leaked secrets and unsafe APIs, detect breaking changes, triage issues, generate changelogs, and assess repository hygiene through native stdio JSON-RPC tools.7Apache 2.0
- AlicenseAqualityDmaintenanceEnables analyzing Salesforce deployment logs, validating metadata manifests, assessing permission risks, and generating remediation plans through deterministic, local-only rules.4MIT
- AlicenseNot gradedqualityCmaintenanceEnables an AI to risk-review Terraform and OpenTofu plan files over MCP stdio by summarizing plans, ranking dangerous changes, diffing resources with secrets masked, and comparing two plans without running Terraform.MIT
- 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
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.