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DimensionCodescene MCP ServerExpose CodeScene's Code Health analysis as local AI-friendly tools.MartinLoop MCP ServerAn independent control layer that gives AI coding agents budgets, brakes, and receipts
FMRS46 / 100 · D79 / 100 · B
Reliability7 / 2012 / 20
Security and permissions8 / 2018 / 20
Maintenance6 / 2016 / 20
Documentation13 / 2018 / 20
Setup experience12 / 2015 / 20
Best for
  • Teams integrating AI-driven development with code quality analysis
  • Developers aiming to safely refactor legacy code with AI assistance
  • Organizations embedding code health checks in CI/CD pipelines
  • Engineering teams scaling AI coding agents while controlling spend
  • Platform teams that need auditable, rollback-aware agent execution records
  • Organizations using multiple agents (Claude, Codex, Gemini) under one governed flow
Not for
  • Users without a CodeScene account or standalone license (though a standalone Code Health MCP is available)
  • Scenarios requiring zero network dependency after setup (analysis is local but fetches data from CodeScene)
  • Teams needing a complete replacement for CodeScene's platform features
  • Developers who just want a simple code-completion helper without run governance
  • Teams unwilling to accept local JSONL run records with HMAC signing
  • Environments without a supported adapter (Claude/Codex/Gemini CLIs, direct provider, or verifier-only)
Required permissions
  • Requires CS_ACCESS_TOKEN to fetch data from a CodeScene instance
  • Read access to the local codebase for analysis
  • Volume mount for Docker installations to access code
  • Environment variables for custom SSL certificates (e.g., REQUESTS_CA_BUNDLE)
  • Can set CS_DISABLE_VERSION_CHECK to disable version checks
  • Read/write access to the configured repo root (writable scope restrictable via --allow-path/--deny-path)
  • Ability to invoke the chosen agent CLI (Claude/Codex/Gemini, etc.) and its underlying model account
  • Local disk writes for run records, receipts, and share outputs (share/run-receipt., etc.)
  • Execution of user-configured verifier commands (e.g. npm test)
Risks and side effects
  • Transmission risk: Analysis results are fetched via REST from your own CodeScene account; source code is not sent externally, but token security is critical.
  • Dependency risk: A valid access token is required; token leakage could expose data.
  • Model limitations: Older models may not adhere to MCP constraints, affecting refactoring quality.
  • Path hallucination: IntelliJ's AI assistant may provide incorrect MCP paths, causing connection issues.
  • Verifier commands are executed; poorly chosen verifier commands carry security risk despite preflight policy checks
  • Cost and token figures may be estimates (labeled with provenance) and should not be treated as settled accounting
  • Receipt integrity must be verified before use as trustworthy evidence; missing rollback evidence is flagged as EVIDENCE_BOUNDARY
  • Policy defaults come from martin.config.yaml and can be overridden by CLI flags; misconfiguration can loosen safety boundaries
Supported clientsClaude Desktop, VS Code, JetBrains IntelliJ, GitHub Copilot, CursorClaude Code, Codex, Gemini CLI, Cursor, VS Code, GitHub Copilot, Continue
Tools50