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

DimensionContext ModeOptimize context windows for AI coding agents.MartinLoop MCP ServerAn independent control layer that gives AI coding agents budgets, brakes, and receipts
FMRS54 / 100 · D79 / 100 · B
Reliability10 / 2012 / 20
Security and permissions8 / 2018 / 20
Maintenance10 / 2016 / 20
Documentation14 / 2018 / 20
Setup experience12 / 2015 / 20
Best for
  • Developers using one of the supported AI coding CLIs, IDEs, or agents
  • Teams working with large codebases, logs, web pages, or batch queries
  • Users who need work-state recovery after context compaction
  • 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 requiring a dedicated GUI MCP integration not listed as supported
  • Users who only need a simple MCP tool and do not want hooks, plugins, or local indexing
  • Environments that prohibit local script execution or access to local files, credentials, and network resources
  • 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 Node.js, Bun, or another listed runtime to execute locally.
  • Sandboxed scripts can read and process local files and can use authenticated CLIs such as gh, aws, gcloud, kubectl, and docker through inherited environment variables and configuration paths.
  • ctx_fetch_and_index requires URL fetching; indexed content is stored in SQLite databases under the user's local data directory.
  • With hooks or plugins enabled, the server can intercept selected tool calls, record session events, and apply routing rules.
  • 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
  • Tools such as ctx_execute run code supplied by the user or agent; that code may read local files, invoke commands, or use inherited credentials.
  • Session and indexed content persist in local SQLite databases; ctx_purge permanently deletes all indexed content.
  • Hooks may block or redirect selected tool calls, and routing capabilities vary by client.
  • The README's savings percentages are project-reported metrics; actual results depend on tool output and usage.
  • 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, Gemini CLI, VS Code, JetBrains Copilot, GitHub Copilot CLI, Cursor, OpenCode, KiloCode, OpenClaw / Pi Agent, Codex, Kimi Code, Qwen Code, Antigravity IDE, Antigravity CLI, Kiro, Zed, Pi Coding Agent, OMPClaude Code, Codex, Gemini CLI, Cursor, VS Code, GitHub Copilot, Continue
Tools110