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DimensionContext+ MCP ServerSemantic Intelligence for Large-Scale Engineering.MartinLoop MCP ServerAn independent control layer that gives AI coding agents budgets, brakes, and receipts
FMRS48 / 100 · D79 / 100 · B
Reliability8 / 2012 / 20
Security and permissions8 / 2018 / 20
Maintenance12 / 2016 / 20
Documentation10 / 2018 / 20
Setup experience10 / 2015 / 20
Best for
  • Developers working on large codebases
  • Teams needing highly accurate code understanding
  • Users leveraging Ollama or OpenAI-compatible embeddings
  • Those who want AI-assisted code editing with rollback capability
  • 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
  • Small projects where this might be overkill
  • Users needing native git integration for version control (only shadow restore points)
  • Scenarios requiring non-code operations like SQL or filesystem access
  • 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
  • Read project files (via AST parsing and file traversal)
  • Create shadow restore points (in .mcp_data directory)
  • Run linters and compilers (run_static_analysis)
  • Write code files (via propose_commit)
  • Network access (calling Ollama or OpenAI-compatible APIs)
  • 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
  • propose_commit may modify code files; validation rules exist but caution is advised
  • Runtime cache (.mcp_data) can consume disk space
  • Embeddings rely on local or cloud models; consider data privacy
  • Spectral clustering results may be unstable; labels may be inaccurate
  • 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, Claude Code, Cursor, VS Code, Windsurf, OpenCodeClaude Code, Codex, Gemini CLI, Cursor, VS Code, GitHub Copilot, Continue
Tools170