# Wolfe-Jam/claude-faf-mcp [Health: Active]

**Category:** 🧠 Knowledge & Memory  
**Repository:** https://github.com/Wolfe-Jam/claude-faf-mcp  
**GitHub Stars:** 23  
**npm Downloads (last month):** 2333  
**Views:** 4  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/wolfe-jam-claude-faf-mcp

## Description
First & only persistent project context MCP. Provides .faf (Foundational AI-context Format) Project DNA with 33+ tools, Podium scoring (0-100%), and format-driven architecture. Official Anthropic Registry. 10k+ npm downloads.

## Tools
Capabilities this server exposes over MCP:

- **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
- **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

## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `bunx` (confidence: high):

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

## Documentation

## What Wolfe-Jam/claude-faf-mcp MCP server does

Wolfe-Jam/claude-faf-mcp MCP server manages persistent project context in the `.faf` format, described in the repository as Foundational AI-context Format. The context is intended to capture both repository information and human-supplied intent, including the six project questions commonly represented as who, what, why, where, when, and how.

The server exposes a core set of MCP tools for creating, reading, checking, scoring, and synchronizing this context. It can initialize project DNA, inspect a project’s technology stack, derive information from a README, or load context from a GitHub repository URL. Human context can be added separately rather than inferred from source files.

Its scoring tools report an AI-readiness result from 0 to 100% with a breakdown. `faf_bench` compares agent grounding with and without the `.faf` file and emits a receipt describing the result. The available tools also include project status, environment inspection, file reads and writes, and an explanation of the `.faf` format.

## How it works

The repository treats `.faf` as the primary context source. `faf_init`, `faf_auto`, and `faf_quick` provide different entry points for creating project context, while `faf_go` guides the user through an interview intended to improve the result. `faf_readme` and `faf_git` add evidence from existing project documentation or a remote GitHub repository.

After context is created, `faf_check` validates its structure and `faf_doctor` diagnoses and fixes common problems. `faf_score` measures the resulting context, and `faf_status` provides a project-level view.

Wolfe-Jam/claude-faf-mcp MCP server can synchronize `.faf` into `CLAUDE.md`. Its synchronization options also cover AGENTS.md, `.cursorrules`, GEMINI.md, and `.github/copilot-instructions.md`. A tri-sync operation connects `.faf`, `CLAUDE.md`, and `MEMORY.md`.

## Setup and configuration

The project is published as the npm package `claude-faf-mcp` and is licensed under MIT. The repository describes the server as a TypeScript MCP server and includes an npm package badge. A package-runner configuration can start it with:

```bash
npx -y claude-faf-mcp
```

The README distinguishes between a core tool set and an expanded set: it states that the server has 12 core tools and 34 tools when `FAF_TOOLS=all` is used.

## Tools and capabilities

The listed capabilities include:

- Initialize, auto-populate, or quickly create project DNA.
- Extract context from README files and GitHub repository URLs.
- Add human context using the six project questions.
- Score AI readiness and benchmark grounding with a receipt.
- Validate `.faf` structure and diagnose common issues.
- Read and write files, inspect the environment, and show project status.
- Import or export AGENTS.md, `.cursorrules`, GEMINI.md, and Conductor content.
- Synchronize project context into files used by several coding assistants.

Wolfe-Jam/claude-faf-mcp MCP server is most relevant when repository code alone does not express project intent, or when several agent clients need a shared, generated context surface.

## Limitations and notes

Although the tools include GitHub URL extraction, no GitHub credential requirement is stated. The README refers to version-specific composition with `faf-cli`, but the exact dependency installation and version-management procedure is not included here.

_Full upstream README: https://allmcps.com/mcp/wolfe-jam-claude-faf-mcp/readme_