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DimensionFirebase MCP ServerFirebase-specific capabilities for AI coding toolsMartinLoop MCP ServerAn independent control layer that gives AI coding agents budgets, brakes, and receipts
FMRS59 / 100 · C79 / 100 · B
Reliability10 / 2012 / 20
Security and permissions9 / 2018 / 20
Maintenance16 / 2016 / 20
Documentation11 / 2018 / 20
Setup experience13 / 2015 / 20
Best for
  • Developers already using the firebase-tools CLI who want an AI assistant to perform common Firebase operations
  • Teams doing Firebase integration work from within a local project directory
  • 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 an existing Firebase project or local authentication set up
  • Teams that require strict approval gates before an AI agent can trigger production deploys or data deletions
  • 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
  • Local filesystem access (to read the project directory and files like firebase.json)
  • Firebase/Google Cloud credentials (user login session, service account key, or application default credentials)
  • Network access to the target Firebase project
  • 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
  • firebase-tools supports destructive operations (e.g. firestore:delete, database:remove) and production deploys, so overly broad credentials could let an AI agent cause data loss or affect a live project
  • Service account keys and long-lived user tokens are highly sensitive credentials if leaked
  • Neither the README nor server.json enumerates the specific MCP tools exposed, so the exact operation surface should be verified before use
  • 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 clientsCursorClaude Code, Codex, Gemini CLI, Cursor, VS Code, GitHub Copilot, Continue
Tools00