claude-faf-mcp v5.6

Home: faf.one/mcp Live demo: claude-faf-mcp.vercel.app

Persistent Project Context for Claude. 30 seconds. 🐘 Nelly Never Forgets.

FAF defines. MD instructs. AI codes.

🐘 tri-sync now free for all builders — .faf ↔ CLAUDE.md ↔ MEMORY.md in one command. Pro feature. Now free.

⚡ New: /faf prompt — type /faf in Claude Desktop. It checks your project, scores it, drives it to 100%, and syncs. Relentlessly. One command.

🏆 v5.6.1 — FAF-binary scoring has landed. faf_score now reads faf-cli's real scorer directly. Same number your AI and faf score (CLI) see. Truthful, IANA-spec, deterministic.

33 MCP tools. IANA-registered formats (application/vnd.faf+yaml · application/vnd.fafm+yaml). 2,346 test executions per push.

The 3Ws — 3 Answers. That's It.

Every great product started with 3 answers to the 3Ws — Who, What, Why:

Same pattern. Every product that works starts here. .faf captures it:

human_context:
  who: "people who need a ride across town"
  what: "tap a button, car arrives in minutes"
  why: "taxis are slow, expensive, and hard to find"

30 seconds. Claude builds your project.faf from this. Every session after, AI starts smart.

The 6Ws — For Optimized AI

3Ws gets you started. For fully optimized AI, complete the set — Where, When, How:

  where: "mobile app, iOS and Android"    # where does it live?
  when: "launch in 3 months"              # when is it shipping?
  how: "GPS matching, real-time pricing"  # how does it work?

3Ws initiates the project with AI. 6Ws optimizes AI to 100%. Same YAML, same file. More examples → faf.one/ideas

Quick Start

faf-cli — universal (any AI)

npx faf-cli auto

Same .faf, every surface — Claude, Gemini, Grok, Cursor. faf-cli on npm →

Claude Desktop — click, copy, paste, install

Click — one-click .mcpb

⬇ Download claude-faf-mcp-5.5.2.mcpb

Double-click. No terminal. No JSON config. 33 tools live in 10 seconds.

Copy — paste-prompt to Claude

Install the FAF MCP server: npm install -g claude-faf-mcp, then add this to my claude_desktop_config.json: {"mcpServers": {"faf": {"command": "bunx", "args": ["claude-faf-mcp"]}}} and restart Claude Desktop.

Paste — claude_desktop_config.json

{
  "mcpServers": {
    "faf": { "command": "bunx", "args": ["claude-faf-mcp"] }
  }
}

Install — manual npm

npm install -g claude-faf-mcp

Restart Claude Desktop.

Then

Type /faf — Claude checks your project, scores it, drives it to 100%, and syncs. Done.

Or tell Claude your 3Ws: "I'm building [what] for [who] because [why]"

How It Works

You → 3 answers → project.faf → AI reads it → every session → forever

project.faf  ←── 8ms ──→  CLAUDE.md     (bi-sync, free)
project.faf  ←── 8ms ──→  MEMORY.md     (tri-sync, Pro 🐘)

Claude does the rest. Zero-effort, right first time, fast, accurate, done. Language, framework, package manager, build tools — all auto-detected from your existing files. The human context is the part only you can give.

Scoring: From Blind to Optimized

At 55%, AI guesses half the time. At 100%, AI knows your project. Same compiler as faf-cli — same score everywhere.

33 MCP Tools

All tools run standalone — zero CLI dependencies, 19ms average execution.

Create & Detect

Validate & Score

Sync & Persist

Export & Interop

Read & Write

Full tool reference →

🐘 Nelly Never Forgets

bi-sync keeps .faf ↔ CLAUDE.md aligned.

tri-sync adds MEMORY.md — your AI remembers your project across every session.

bi-sync  = .faf ↔ CLAUDE.md              ← always in sync
tri-sync = .faf ↔ CLAUDE.md ↔ MEMORY.md  ← Nelly never forgets 🐘

Pro feature, free for developers. Teams & Enterprise: faf.one/pro (plans)

The .FAF Position

Model        Context          Protocol
─────        ───────          ────────
Claude    →   .faf        →    MCP
Gemini    →   .faf        →    MCP
Codex     →   .faf        →    MCP
Any LLM   →   .faf        →    MCP

IANA-registered (application/vnd.faf+yaml). Works with any AI. Define once, use everywhere.

Ecosystem

Same project.faf. Same scoring. Same result. Different execution layer.

Quality

391 tests · 12 suites · 6 platforms (ubuntu/macos/windows × Node 18/20)

CI Dashboard →

Privacy

Everything runs locally. No data leaves your machine. No analytics, no telemetry, no tracking, no accounts. Privacy policy →

If claude-faf-mcp has been useful, consider starring the repo — it helps others find it.

Citation

If you use claude-faf-mcp or the .faf / .fafm formats in research or production, please cite the format papers:

Wolfe, J. (2025). Format-Driven AI Context Architecture: The .faf Standard for Persistent Project Understanding. Zenodo. https://doi.org/10.5281/zenodo.18251362

Wolfe, J. (2026). Permanent Memory and Instant Recall: The .fafm Standard for Multi-Profile AI Agent Memory. Zenodo. https://doi.org/10.5281/zenodo.20348942

BibTeX

@article{wolfe2025faf,
  title     = {Format-Driven AI Context Architecture: The .faf Standard for Persistent Project Understanding},
  author    = {Wolfe, James},
  year      = {2025},
  month     = {nov},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.18251362},
  url       = {https://doi.org/10.5281/zenodo.18251362}
}

@article{wolfe2026fafm,
  title     = {Permanent Memory and Instant Recall: The .fafm Standard for Multi-Profile AI Agent Memory},
  author    = {Wolfe, James},
  year      = {2026},
  month     = {may},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.20348942},
  url       = {https://doi.org/10.5281/zenodo.20348942}
}

License

MIT — Free and open source

FAF Family

format | driven 🏎️⚡️ wolfejam.dev

Get the CLI

faf-cli — The original AI-Context CLI. A must-have for every builder.

npx faf-cli auto

Anthropic MCP #2759 · 2 IANA registrations: vnd.faf+yaml (Context) · vnd.fafm+yaml (Memory) · faf.one · npm