Shipshape MCP Server
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Alternatives to Shipshape MCP Server
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Related Servers
- AlicenseNot gradedqualityBmaintenanceLets a coding agent lint a Dockerfile or Kubernetes manifest through the Shipshape API, returning an A–F grade, score, pass/fail status, and every finding with its line, fix, and reference. Catches issues like root in the final stage, unpinned base images, curl | sh, secrets in ENV/ARG, and Pod Security Standards, resources, and probes violations before handing work over.MIT
- AlicenseAqualityCmaintenanceEnables agents to validate Dockerfiles for build-breaking syntax errors, risky patterns like root users and secrets in ENV/ARG, and cache-order problems, returning every finding with its line and a fix. It also verifies referenced container images against public registries, reporting tag existence, digests to pin, supported platforms, last rebuild time, and end-of-life status with an upgrade tag.21MIT
- AlicenseNot gradedqualityBmaintenanceExposes a Dockerfile auditing engine to AI assistants, letting them scan a Dockerfile for the 32 deterministic layer-aware rules, print a stage/layer map showing what each layer adds or deletes, explain any rule's rationale and fix, and list available rules. Findings include file:line evidence so agents can surface and triage image-safety issues directly in Claude Code, Cursor, or Cline.MIT

EVIDIQ Bastionofficial
AlicenseNot gradedqualityBmaintenanceAudits infrastructure configurations (Dockerfiles, GitHub Actions, Kubernetes, Terraform/Compose) against 14 deterministic security rules, returning BLOCK/REVIEW/PASS verdicts and signing attestations. Helps agents verify deployment configs before applying them.1MIT- AlicenseNot gradedqualityAmaintenanceEnables AI agents to submit code files for analysis and receive rule-based findings with severities, line numbers, and SARIF fingerprints, covering security, complexity, and dependency/configuration drift.682 PyPI7MIT
- AlicenseBqualityAmaintenanceEnables Cursor and Codex agents to audit Agent Skill structure (frontmatter, broken links, missing files), analyze dependencies for undeclared, unused or vulnerable packages via OSV, and run 28 local bug and code-quality rules with SonarQube-style A–E ratings. It can also optionally pull issues, metrics and quality-gate status from SonarQube or SonarCloud, or scan and upload results.92MIT
TDQS
Scored across 1 tool
There is only one tool, so there is no possibility of misselection between tools. The single tool's purpose (lint a Dockerfile or Kubernetes manifest) is clearly stated, and its two input modes (`content` vs `path`) are handled as parameters rather than as separate overlapping tools.
With a single tool named `lint`, there is no mixed convention to violate; the name is a clear, conventional verb. Consistency is trivially satisfied, though there is no multi-tool pattern to demonstrate predictability.
One tool is on the thin side, but the server's scope is genuinely narrow (static analysis of Dockerfiles and K8s manifests), so a single entry point is defensible. It sits at the borderline of '1-2 tools feels thin' rather than being an extreme mismatch, since the tool itself does substantial work (grade, score, findings, fixes, references).
The core linting lifecycle — submit a manifest and get scored findings with fixes and references — is covered for both Dockerfile and Kubernetes inputs. However, obvious adjacent operations are absent: auto-fix/remediation, batch or directory-level linting, and any rule/config discovery, which leaves agents at a dead end once findings are returned.