MCP Server

Active

MIT

Claude FAF MCP Server

one.faf/claude-faf-mcp

Persistent project context for Claude with IANA-registered .faf format and AI-readiness scoring.

What is the Claude FAF MCP server?

The Claude FAF MCP server provides persistent project context management for Claude using the IANA-registered .faf format. It detects your project's stack, scores AI-readiness (0-100%), and syncs context across sessions so Claude starts every conversation grounded in your project's goals and architecture. Core 14 tools handle initialization, scoring, memory, and synchronization with optional extended tools for deeper integration.

Claude FAF keeps your project context alive across sessions. Answer three questions (Who, What, Why) and the server auto-detects your stack, scores how well Claude understands your project, and writes a persistent .faf file that travels with your code. Every Claude session starts with fresh context instead of a blank slate. Teams benefit from committed context that grounds every teammate's AI from day one.

How to install Claude FAF

Copy-paste configuration for popular MCP clients.

transport: stdio

Config generated by PluginBench — verify against the source before use.

~/Library/Application Support/Claude/claude_desktop_config.json

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

Tools & capabilities

Tools this server exposes to the agent.

faf_init — Create project.faf for a folder with auto-detected stack
faf_auto — Fill project.faf from repo files, then write CLAUDE.md
faf_go — Answer the goal and 6Ws (Who, What, Why, Where, When, How) interactively
faf_score — Calculate AI-readiness score (0-100%) from faf-cli
faf_bench — Benchmark AI grounding improvement with and without the .faf
faf_doctor — Diagnose project.faf issues with fixes
faf_trust — Validate project.faf and return a trust receipt
faf_sync — Write CLAUDE.md from project.faf; optionally write AGENTS.md, .cursorrules, GEMINI.md
faf_tri_sync — Write faf context into MEMORY.md for cross-session memory (🐘 Nelly Never Forgets)
faf_setup — Install SessionStart hook for automatic context refresh on every Claude Code session
faf_context — Show or set active project; detail returns .faf text
faf_etch — Remember decisions across sessions in project soul (soul.fafm)
faf_recall — Recall memories from the project soul
faf_about — Explain the .faf format

Use cases

Answer 3 questions about your project and auto-generate a scored context file that Claude reads every session
Benchmark how much better Claude performs with your .faf context vs. without it
Commit project.faf to git so every teammate's Claude starts with identical grounding and no re-explanation needed
Track project decisions and goals in persistent memory across multiple Claude Code sessions
Sync your project context to multiple AI platforms (Claude, Gemini, Cursor) from a single .faf file

Claude FAF MCP server FAQ

What is the Claude FAF MCP server?

It's a persistent context manager for Claude that uses the IANA-registered .faf format. You answer 3 questions (Who, What, Why), the server detects your stack and scores AI-readiness (0-100%), then syncs context so Claude starts every session grounded in your project.

Is it free?

Yes. The core 14 tools and .faf format are free for developers. Teams and Enterprise plans are available at faf.one/pro for advanced features like MEMORY.md tri-sync.

How do I install it in Claude Desktop?

Download the .mcpb file from the releases page and open it in Claude Desktop, or add to claude_desktop_config.json: {"mcpServers": {"faf": {"command": "npx", "args": ["-y", "claude-faf-mcp"]}}}

How do I use it in Claude Code?

Run: claude mcp add faf -- npx -y claude-faf-mcp, then use the /mcp__faf prompt to score your project and answer the 3Ws.

Does it require authentication or send data to FAF servers?

No. Everything runs locally. The only network use is cloning a repo you explicitly name with faf_git. Your context stays in your repo.

What's the difference between faf_sync and faf_tri_sync?

faf_sync writes CLAUDE.md from project.faf so Claude reads it each session. faf_tri_sync also writes MEMORY.md so Claude remembers decisions and context across sessions (🐘 Nelly Never Forgets).

README (reference)

Source of truth, from the repository.

claude-faf-mcp

Home: faf.one/mcp Site: claude.faf.one

Persistent Project Context with Memory, looped for you. One-click setup. 30 seconds. 🐘 Nelly Never Forgets.

FAF defines. MD instructs. AI codes.

⭐ Bookmarks it for you, helps other devs find it too.

v0.2-conformant reader of the FAF Context Ingestion Contract.

🐘 tri-sync | .faf → MEMORY.md (faf_tri_sync), alongside .faf → CLAUDE.md (faf_sync).

⚡ The faf prompt — pick it from your host's prompt list (Claude Code shows it as /mcp__<server name>__faf). It scores your project, fills what the repo can, asks you only what only you can answer, verifies, and syncs.

7.0.0 is a major release — scores can move. claude-faf-mcp now scores with faf-cli 8's always-33 engine. A .faf without the 12 enterprise slotignored markers counts them as empty (21 filled = 64%): run faf_auto and it writes the markers. The npx config and the SessionStart hook are not pinned to a version, so an install that runs npx -y claude-faf-mcp moves to 7.x on its next start. Every change is in the CHANGELOG.

Context for Claude: faf-cli writes this repo's CLAUDE.md from its scored project.faf — faf_sync here, faf sync in faf-cli. See FAF-CLI for Claude Code 👀.

Composes faf-cli (the version is pinned in package.json). Detection, scoring, the renders and every writer are faf-cli's own functions, loaded as a dependency; claude-faf-mcp does not fork them and never runs a faf found on your PATH.

Core 14 MCP tools (30 with FAF_TOOLS=all). IANA-registered formats (application/vnd.faf+yaml · application/vnd.fafm+yaml).

What's New in v7.0.0 — The Always33 Edition

One engine, one number: claude-faf-mcp 7 scores with faf-cli 8's always-33 kernel — the same score faf-cli and faf-kernel give.

The always-33 engine. Every tool scores with faf-cli 8.0.0 — all 33 Mk4 slots, one Rust kernel. Checked live: faf-python-sdk 56, mcp-context-card 56, faf-cli ✪ 100, the same numbers faf-cli and the reference scorer give.
Your 21 slots, and the 12 enterprise slots in view. The enterprise slots (infra, app, ops) are marked slotignored unless your app-type uses them. faf_score scores against all 33; slotignored slots drop out of the denominator.
Upgrading: a .faf without the 12 markers now scores against 33. Run faf_auto — it writes them and your score returns (56% → ✪ 100% on a real v7-era file).

v6.0.0 — The Earned Badge Edition

Every badge earned, none claimed: claude-faf-mcp 6.0 composes faf-cli, touches only what it wrote, and every tool tells the truth — one score, facts from repo, nothing from your PATH.

Composes faf-cli 7.13. Detection, scoring, the renders and every writer are faf-cli's own functions. claude-faf-mcp never runs a faf it finds on your PATH.
Touches only what it wrote.
- Every write is atomic and goes through faf-cli's safe path, and a file that links out of the project is refused.
- project.faf edits keep your comments and exact values.
- Your soul.fafm and Claude's own MEMORY.md notes stay as you left them.
One score. Every tool shows faf-cli's score, and ✪ appears only at 100%.
Facts from repo. Every empty slot says what fills it:
- a fact from repo, which faf_auto writes;
- no fact in repo, which you answer with faf_go;
- or yours, for the 6Ws.
Tools that tell the truth.
- Every title, hint and schema matches what the tool does, and bad arguments are refused before anything runs.
- Core 14: faf_setup and faf_tri_sync join the default list.
Safe with any repo.
- A cloned repo's symlinks never reach your AI's context.
- faf_go answers can't pollute objects.
- The file tools stay inside the active project.
Ships what it runs. The .mcpb runs the server bundled inside it, and it's started and checked before its sha is recorded. Node 22+, with CI on Node 22 and 24 across Ubuntu, macOS and Windows.
Retired: faf_clear, faf_friday, faf_guide, faf_write, the interop imports into project.faf, and faf_check protect/unlock. The archive tag archive/cfm-v5-surface keeps them.

The 3Ws — 3 Answers. That's It.

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

	WHO is it for?	WHAT does it do?	WHY build it?
Uber	People who need a ride	Tap a button, car arrives	Taxis were broken
Airbnb	Travelers who can't afford hotels	Stay in someone's spare room	Millions of empty rooms exist
Slack	Teams drowning in email	Organized group messaging	Decisions buried in threads
Venmo	Friends splitting bills	Send money instantly	Someone always forgets to pay back

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

Needs Node 22 or later.

Claude Desktop — one click

⬇ Download claude-faf-mcp-7.0.0.mcpb

Open it in Claude Desktop. The extension runs the server bundled inside it (no npx, no network at start) and lists the Core 14 tools.

Claude Desktop — config

Add to claude_desktop_config.json, then restart Claude Desktop:

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

After npm install -g claude-faf-mcp you can use the installed bin instead: { "command": "claude-faf-mcp" }. With Bun on Claude Desktop's PATH, { "command": "bunx", "args": ["claude-faf-mcp"] } works too.

Claude Code

claude mcp add faf -- npx -y claude-faf-mcp

Pinning

The npx config and the SessionStart hook faf_setup installs (npx -y claude-faf-mcp --session-refresh) are not pinned: they run the latest release, so fixes arrive without a reinstall, and a new major arrives the same way. To stay on a major, write it in your config yourself: "args": ["-y", "claude-faf-mcp@6"]. The .mcpb runs the version it was built from.

Then

Run the faf prompt — Claude scores your project, fills what the repo can, asks you what only you can answer, verifies and syncs.

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

faf-cli — any terminal

npx faf-cli auto

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

How It Works

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

project.faf  ──→  CLAUDE.md     (faf_sync)
project.faf  ──→  MEMORY.md     (faf_tri_sync 🐘)

Language, framework, package manager, build tools — faf-cli detects them from your existing files. The human context is the part only you can give.

For Claude Code teams

.faf lives in the repo. Your context travels with the code — committed, versioned, done.

Every session starts grounded. Install the native SessionStart hook once (faf_setup — preview first, your settings preserved). After that, every Claude Code session opens with a one-line heartbeat instead of a blank slate:

faf: context ✪ 100% — fresh · +7 intent the code can't carry

That line is the relay: Claude already knows your stack and your score — and the +N is the intent the code can't carry: the goal and 6Ws only you can give or confirm. No re-explaining "what this project is" at the top of every session.

It scales to the team by construction:

commit project.faf  →  every teammate's Claude starts with the same context
git clone           →  a new dev's Claude is grounded before they write a line

One source of truth. faf_sync writes CLAUDE.md from .faf — only its faf-managed block, so your own notes stay put. Add MEMORY.md for cross-session memory (tri-sync 🐘).
No drift. The score is deterministic — same .faf, same number, on every machine and in CI. A teammate can't be accidentally less grounded than you.
Local. No accounts, no telemetry, nothing sent to FAF. The one network use is cloning a repo you name, only when you ask (privacy). The context is yours; it rides in the repo.

Onboarding becomes git clone → grounded. The context a new teammate would normally pick up by asking around is already in the repo, machine-readable, from the first clone.

Scoring: From Blind to Optimized

Tier	Score	What it means
✪ TROPHY	100%	Gold Code — AI is optimized
★ GOLD	99%+	Near-perfect context
◆ SILVER	95%+	Excellent
◇ BRONZE	85%+	Production ready
● GREEN	70%+	Solid foundation
● YELLOW	55%+	AI flipping coins
○ RED	<55%	AI working blind
♡ WHITE	0%	No context at all

At 55%, AI guesses half the time. At 100%, AI knows your project. The score is faf-cli's scoreFafYaml — the always-33 number faf score prints for the same file.

MCP Tools — Core 14, 30 with `FAF_TOOLS=all`

By default claude-faf-mcp lists the Core 14 — the lifecycle tools you reach for. Set FAF_TOOLS=all to list the Extended tools too; every tool is callable by name either way. Retired in 6.0.0: faf_clear, faf_friday, faf_guide and faf_write (a call by name returns one line naming what to use instead), the AGENTS.md / .cursorrules / GEMINI.md / conductor imports into project.faf, and faf_check protect/unlock.

Every tool runs on the faf-cli this package depends on. Nothing is run from your PATH.

Core

Tool	Purpose
`faf_init`	Create project.faf for a folder (faf-cli detects the stack)
`faf_auto`	Fill project.faf from the repo's own files, then CLAUDE.md
`faf_go`	The goal and the 6Ws, by question and answer
`faf_score`	AI-readiness score (0-100%), from faf-cli
`faf_bench`	Benchmark AI grounding — cold vs with the .faf, graded mechanically, with a receipt
`faf_doctor`	Diagnose project.faf: each finding with the tool that fixes it
`faf_trust`	Validate project.faf and return a trust receipt for its score
`faf_sync`	Write CLAUDE.md from project.faf — `agents`/`cursor`/`gemini`/`copilot`/`all` also write AGENTS.md / .cursorrules / GEMINI.md / copilot-instructions.md
`faf_tri_sync`	Write faf's block into the MEMORY.md Claude Code loads for this project 🐘
`faf_setup`	Install the SessionStart hook in the project settings (preview first)
`faf_context`	Show or set the active project; `detail` returns the .faf text
`faf_etch`	Remember a decision across sessions (the project soul, soul.fafm)
`faf_recall`	Recall memories from the project soul
`faf_about`	What the .faf format is

Extended (FAF_TOOLS=all)

Tool	Purpose
`faf`	Start here: the project, its score and the steps to 100% (reads only)
`faf_quick`	Create project.faf from one line: name, goal, language, framework, hosting
`faf_readme`	Read the 6Ws from README.md; `apply` fills only empty slots
`faf_human_add`	Set one 6W slot in project.faf
`faf_formats`	The formats faf-cli finds in the folder, and what faf_auto would write (dry run)
`faf_git`	Author a project.faf from a repo URL (clones it with git — uses the network)
`faf_check`	faf-cli's validateFaf and the state of every slot
`faf_dna`	The project's .faf-dna lineage (reads only)
`faf_status`	Whether the project has a .faf, with its first lines
`faf_agents`	Write AGENTS.md (OpenAI Codex and other agents)
`faf_cursor`	Write .cursorrules (Cursor IDE)
`faf_gemini`	Write GEMINI.md (Google Gemini CLI)
`faf_conductor`	Write Google Conductor's conductor/ files
`faf_read`	Read a file inside the active project
`faf_list`	List a folder inside the active project
`faf_debug`	The active project, write access and the bundled faf-cli version

🐘 Nelly Never Forgets

faf_sync writes CLAUDE.md from .faf, so the two stay aligned.

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

faf_sync = .faf → CLAUDE.md              ← written from .faf
tri-sync = .faf → MEMORY.md   (faf_sync writes CLAUDE.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). One file, one format. Define once, use everywhere.

Ecosystem

Package	Platform	Registry
claude-faf-mcp (this)	Claude	npm
faf-cli	CLI	npm + Homebrew
gemini-faf-mcp	Google Gemini	PyPI
grok-faf-mcp	xAI Grok	npm
rust-faf-mcp	Rust	crates.io
faf-wasm	Browser/Edge	npm
Chrome Extension	Browser	Chrome Web Store

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

Quality

Tests run with bun on ubuntu, macOS and Windows; the built package is packed, installed and started on Node 22 and 24 on all three. CI →

Privacy

claude-faf-mcp runs on your machine. No analytics, no telemetry, no accounts. Its one network use is faf_git, and only when you ask it to read a repo: git clones it from the URL you give. The files it writes are listed in the 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 / .fafa 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

Wolfe, J. (2026). Why Agents Need a Passport: .fafa — Portable Identity for the Agentic Era. Zenodo. https://doi.org/10.5281/zenodo.21951641

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}
}

@article{wolfe2026fafa,
  title     = {Why Agents Need a Passport: .fafa — Portable Identity for the Agentic Era},
  author    = {Wolfe, James},
  year      = {2026},
  month     = {aug},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.21951641},
  url       = {https://doi.org/10.5281/zenodo.21951641}
}

License

MIT — Free and open source

FAF Family


faf-cli	`npx faf-cli init` — create .faf for any project
claude-faf-mcp	MCP server for Claude Desktop
gemini-faf-mcp	MCP server for Gemini CLI
grok-faf-mcp	MCP server for Grok
faf-mcp	MCP server for Cursor, Windsurf, Cline, VS Code
rust-faf-mcp	MCP server in Rust
faf-skills	Claude Code skills for .faf
faf.one	Blog, downloads, docs
IANA: vnd.faf+yaml	Context format (2025-10-30)
IANA: vnd.fafm+yaml	Memory format (2026-05-13)

format | driven 🏎️⚡️ wolfejam.dev

Get the CLI

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

npx faf-cli auto

MCP Registry: one.faf/claude-faf-mcp · 2 IANA registrations: vnd.faf+yaml (Context) · vnd.fafm+yaml (Memory) · faf.one · npm

Zero-Config. Context that's just there — every session.

Related MCP servers

.FAF Context

Active

.FAF Context — persistent project context for Cursor, VS Code and every MCP-compatible IDE.

TypeScript

MIT

View repository →

Gemini FAF

Active

Persistent project context for Google Gemini. Python/FastMCP. IANA-registered .faf format.

Python

MIT

View repository →

Grok FAF

Active

Persistent project context for xAI Grok — URL-based MCP with zero config, IANA-registered .faf format.

TypeScript

MIT

View repository →

Rust FAF

Active

Persistent project context — Rust-native MCP server. IANA-registered .faf format.

Rust

MIT

View repository →

one.faf/wjttc

F1-grade MCP certification. 52 tests, 9 tiers (49 scored + T9 Receipt validation). Ecosystem: #2759

View repository →

one.secureai/redaction

Takes the people out of text before a model sees it, and puts them back in the reply.

View repository →

Claude FAF MCP Server

What is the Claude FAF MCP server?

How to install Claude FAF

Tools & capabilities

Use cases

Claude FAF MCP server FAQ

claude-faf-mcp

What's New in v7.0.0 — The Always33 Edition

v6.0.0 — The Earned Badge Edition

The 3Ws — 3 Answers. That's It.

The 6Ws — For Optimized AI

Quick Start

Claude Desktop — one click

Claude Desktop — config

Claude Code

Pinning

Then

faf-cli — any terminal

How It Works

For Claude Code teams

Scoring: From Blind to Optimized

MCP Tools — Core 14, 30 with FAF_TOOLS=all

🐘 Nelly Never Forgets

The .FAF Position

Ecosystem

Quality

Privacy

Citation

BibTeX

License

FAF Family

Get the CLI

Related MCP servers

.FAF Context

Gemini FAF

Grok FAF

Rust FAF

one.faf/wjttc

one.secureai/redaction

MCP Tools — Core 14, 30 with `FAF_TOOLS=all`