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DimensionCode PathfinderAn MCP server for code intelligence and cross-file taint analysis for security teams.MartinLoop MCP ServerAn independent control layer that gives AI coding agents budgets, brakes, and receipts
FMRS54 / 100 · D79 / 100 · B
Reliability8 / 2012 / 20
Security and permissions10 / 2018 / 20
Maintenance12 / 2016 / 20
Documentation13 / 2018 / 20
Setup experience11 / 2015 / 20
Best for
  • Security teams that need local code-structure analysis and security scanning.
  • Projects using Python, Dockerfiles, or Docker Compose.
  • Development teams that want Claude Code, Cursor, or Cline to access call-graph and security analysis.
  • 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
  • Projects requiring proven stable Go analysis support; the README lists Go as coming soon.
  • Use cases requiring only general language-server functionality without security or code-structure analysis.
  • Integrations that require source-listed MCP tool identifiers or a client-specific configuration example.
  • 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
  • Needs to read and index source code and configuration files under the selected project root.
  • When remote rulesets are used, it needs to download rules; the README says rules are fetched automatically from a CDN.
  • No API keys or cloud accounts are required according to the README.
  • 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
  • Results depend on supported languages, selected rulesets, and code structure; the README marks only Python, Dockerfiles, and Docker Compose as stable.
  • Providing a project to the analyzer allows it to process source code and configuration contents in that project.
  • Automatic CDN rule downloads depend on external network availability.
  • 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 Code, Cursor, ClineClaude Code, Codex, Gemini CLI, Cursor, VS Code, GitHub Copilot, Continue
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