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Compare scores, permissions, risks, and fit in one decision-focused table.

DimensionFileScopeMCPYour AI already knows how to code. Now it knows your codebase.MartinLoop MCP ServerAn independent control layer that gives AI coding agents budgets, brakes, and receipts
FMRS56 / 100 · C79 / 100 · B
Reliability8 / 2012 / 20
Security and permissions12 / 2018 / 20
Maintenance11 / 2016 / 20
Documentation14 / 2018 / 20
Setup experience11 / 2015 / 20
Best for
  • Developers who need deep codebase context for AI assistants.
  • Teams that want to maintain code metadata automatically during development.
  • Projects using multiple languages (supports TS/JS, Python, C/C++, Rust, Go, etc.).
  • 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
  • Scenarios requiring only a simple file list without dependency analysis.
  • Environments without Node.js >=22.
  • Fully offline settings without an LLM (though basic features still work).
  • 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
  • File system read and watch permissions (to scan the codebase).
  • Write permissions (to create `.filescope/data.db` database).
  • Network permissions (optional, to connect to a local LLM endpoint).
  • 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
  • Importance scoring is heuristic and may not align with all project preferences.
  • Regex-based parsing for some languages may be less accurate than AST.
  • LLM summaries depend on external services, potentially raising privacy concerns.
  • Database may consume significant space on large codebases.
  • 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, Cursor AI, CodexClaude Code, Codex, Gemini CLI, Cursor, VS Code, GitHub Copilot, Continue
Tools170