ai-mldeveloper-tools

FAF (File & Project Context)

wolfe-jam

by wolfe-jam

FAF offers free file synchronization software with project context management, automated scoring, health checks, and mul

Provides persistent project context management and file operations with automated project scoring, health assessment, and bi-directional sync capabilities for maintaining documentation and enhancing codebase understanding across multiple programming languages.

github stars

12

0 commentsdiscussion

Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

IANA-registered format for AI contextPrevents context drift with live bi-syncAutomated project scoring to 100% 'Gold Code'

best for

  • / Developers working on complex codebases with AI assistants
  • / Teams maintaining consistent project documentation
  • / Projects requiring persistent AI context across sessions

capabilities

  • / Generate automated project health scores
  • / Create bi-directional sync between .faf files and documentation
  • / Analyze codebase structure across multiple programming languages
  • / Maintain persistent project context for AI interactions
  • / Perform file operations with project awareness
  • / Track documentation drift and alignment

what it does

Maintains persistent project context for AI assistants by automatically analyzing your codebase and syncing documentation to prevent context drift.

how to install

You can install FAF (File & Project Context) in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

FAF (File & Project Context) is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

FAF

claude-faf-mcp

IANA-Registered Format for AI Context · application/vnd.faf+yaml

> **.FAF optimizes AI for your codebase.** At 100% (Gold Code), AI stops guessing and starts knowing. Live bi-sync between `.faf` ↔ `CLAUDE.md` means zero context-drift — your project DNA stays aligned with AI, forever. [![Anthropic MCP](https://img.shields.io/badge/Anthropic_MCP-merged_%232759-blueviolet)](https://github.com/modelcontextprotocol/servers/pull/2759) [![Claude Code](https://img.shields.io/badge/Claude_Code-enabled-00D4D4)](https://github.com/anthropics/claude-code-action) [![CI](https://github.com/Wolfe-Jam/claude-faf-mcp/actions/workflows/ci.yml/badge.svg)](https://github.com/Wolfe-Jam/claude-faf-mcp/actions/workflows/ci.yml) [![NPM Downloads](https://img.shields.io/npm/dt/claude-faf-mcp?label=total%20downloads&color=00CCFF)](https://www.npmjs.com/package/claude-faf-mcp) [![npm version](https://img.shields.io/npm/v/claude-faf-mcp?color=00CCFF)](https://www.npmjs.com/package/claude-faf-mcp) [![Chrome Web Store](https://img.shields.io/badge/Chrome-Extension-4285F4?logo=googlechrome&logoColor=white)](https://chromewebstore.google.com/detail/lnecebepmpjpilldfmndnaofbfjkjlkm) [![Website](https://img.shields.io/badge/Website-faf.one-orange)](https://faf.one) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![project.faf](https://img.shields.io/badge/project.faf-inside-00D4D4)](https://github.com/Wolfe-Jam/faf)

3Ws · Quick Start · Website · DAAFT Analysis · npm · Tools · Tiers · CLI Companion · Changelog

--- ## 💡 The 3Ws — Start Here Nelly Never Forgets Answer 3 questions. That's all your AI needs to start. | Question | What to answer | |----------|---------------| | **👥 WHO** is it for? | The people who will use this. Not you — them. | | **📦 WHAT** does it do for them? | The value they get. Not features — the outcome. | | **🎯 WHY** should it exist? | What's broken today? Why does this need to exist? | **That's it.** Tell Claude your 3Ws and FAF turns them into project DNA that never drifts. **[Build yours → faf.one/ideas](https://faf.one/ideas)** --- ## 📋 The 6 Ws — Quick Reference | Question | Answer | |----------|--------| | **👥 WHO** is this for? | Claude Desktop & Claude Code users, MCP server operators, any MCP client | | **📦 WHAT** is it? | 33-tool MCP server for AI context — IANA-registered format (`application/vnd.faf+yaml`) | | **🌍 WHERE** does it work? | Claude Desktop, Claude Code, any MCP-compatible client | | **🎯 WHY** do you need it? | 91% token waste eliminated, zero context-drift — saves $5,460/year per developer | | **⏰ WHEN** should you use it? | New projects (day one), existing projects (now), exploring repos (instantly) | | **🚀 HOW** does it work? | `npx claude-faf-mcp` — one line in your MCP config | **For AI:** Read the detailed sections below for full context. **For humans:** Use this pattern in YOUR README. **[Build yours →](https://faf.one/6ws)** --- ## Not a Developer? No problem. FAF works for anyone using Claude Desktop. **3 steps:** 1. Install FAF from Claude Desktop → Settings → Extensions 2. Tell Claude your 3Ws: *"I'm building [what] for [who] because [why]"* 3. Claude creates your project DNA — context that never drifts No terminal. No code. Just answer 3 questions. **Try it:** Tell Claude *"Score my project's AI-readiness and tell me how to improve"* — it works on any project, any language, any framework. --- ## The Problem: Context-Drift AI assistants forget. They misunderstand. They drift. Every new session, every new file, every new developer — AI starts guessing again. Your codebase context leaks away. Yesterday's perfect assistant becomes today's confused intern. **The cost:** 91% of tokens wasted on rediscovery. $5,460/year per developer. At 50 devs, that's $273k–$507k annually — before counting project failures from compounding context loss. **[Full DAAFT analysis →](https://faf.one/daaft)** **.FAF fixes this permanently.** --- ## The Solution: Gold Code ``` 🏆 FAF AI-READINESS: 100/100 — GOLD CODE ├─ Project DNA locked in ├─ Zero context-drift ├─ Architecture understood ├─ Eternal bi-sync active └─ Every session starts smart └─ AI works WITH you ``` **Gold Code = AI Optimized.** Your project DNA lives in `project.faf`. AI reads it instantly. Context never drifts. --- ## 🔄 Eternal Bi-Sync (Free Forever) The magic: `.faf` ↔ `CLAUDE.md` stay synchronized in milliseconds. ``` project.faf ←──── 8ms ────→ CLAUDE.md │ │ └── Single source of truth ──┘ ``` - Update either file → both stay aligned - Zero manual maintenance - Works across teams, branches, sessions - **Context never goes stale** ### 🐘 Tri-Sync: Add RAM (Pro) Nelly Never Forgets — Feed Nelly a dime a day bi-sync is core. tri-sync adds more — your AI remembers across sessions. ``` bi-sync = ROM (.faf) ↔ CLAUDE.md ← free forever tri-sync = ROM ↔ CLAUDE.md ↔ RAM (MEMORY.md) ← Pro ``` **Feed Nelly 🐘 — she never forgets.** A dime a day ($3/mo) · a nickel a day ($19/yr) · $29/yr Global (CLI + MCP). 14-day free trial, no signup. **[Friends of FAF → faf.one/pro](https://faf.one/pro)** --- ## Tier System: From Blind to Optimized | Tier | Score | Status | |------|-------|--------| | 🏆 **Trophy** | 100% | **AI Optimized** — Gold Code | | 🥇 **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 is guessing half the time.** At 100%, AI is optimized. --- ## 💎 Lifecycle Value Setup savings get you started. Lifecycle optimization keeps you ahead. | When | Without FAF | With FAF | |------|-------------|----------| | **Day 1** | 20 min setup per dev | 0 min — instant context | | **Month 1** | AI forgets between sessions | AI remembers everything | | **Year 1** | New devs re-explain everything | New devs inherit full context | | **Year 3+** | Institutional knowledge lost | Project DNA preserved forever | **Setup savings: 20 minutes. Lifecycle savings: Infinite.** --- ## ⚡ Quick Start **Copy and paste this to Claude/your AI:** > Install the FAF MCP server: `npm install -g claude-faf-mcp`, then add this to my claude_desktop_config.json: `{"mcpServers": {"faf": {"command": "npx", "args": ["-y", "claude-faf-mcp"]}}}` and restart Claude Desktop. **One-Click Alternative:** [Desktop Extension (.mcpb)](https://github.com/Wolfe-Jam/claude-faf-mcp/releases/latest) --- ## 🛠️ 33 MCP Tools All tools run standalone — zero CLI dependencies, 16.2x faster than process spawning. | Tool | Purpose | |------|---------| | `faf_init` | Initialize project DNA | | `faf_score` | Check AI-readiness (0-100%) | | `faf_sync` | Bi-sync .faf ↔ CLAUDE.md | | `faf_tri_sync` | Tri-sync .faf → MEMORY.md *(Pro — 14-day free trial)* | | `faf_auto` | Auto-detect and populate context | | `faf_enhance` | Intelligent enhancement | | `faf_quick` | Lightning-fast creation (3ms) | | `faf_readme` | Extract 6 Ws from README (+25-35% boost) | | `faf_human_add` | Add human context (Claude Code compatible) | | `faf_agents` | Import/export/sync AGENTS.md (OpenAI Codex) | | `faf_cursor` | Import/export/sync .cursorrules (Cursor IDE) | | `faf_gemini` | Import/export/sync GEMINI.md (Google Gemini) | | `faf_git` | Extract context from any GitHub repo URL | | `faf_conductor` | Import/export Conductor directory | **Performance:** 19ms average execution. Fastest: 1ms. ### ✨ New in v4.5.0: AI Format Interop Define once in `.faf`, generate all four AI instruction formats: ``` project.faf → CLAUDE.md (Anthropic) → AGENTS.md (OpenAI / Linux Foundation) → .cursorrules (Cursor IDE) → GEMINI.md (Google Gemini CLI) ``` **Bi-sync all at once:** ``` faf_bi_sync { all: true } ``` **GitHub context extraction:** ``` faf_git { url: "https://github.com/owner/repo" } → Generates .faf from any public GitHub repo ``` ### 6Ws Builder Answer 6 questions (WHO/WHAT/WHERE/WHY/WHEN/HOW) to boost AI-readiness by +25-35%. Two ways: - **Web:** [faf.one/6ws](https://faf.one/6ws) — Fill form, copy YAML, paste into Claude with `faf_human_add` - **CLI:** `faf 6ws` — Interactive terminal workflow ### README Auto-Extract Already have a README? Extract context automatically: ```javascript faf_readme // Preview extracted context faf_readme { merge: true } // Merge into project.faf faf_readme { merge: true, overwrite: true } // Overwrite existing fields ``` Same +25-35% score boost, zero manual work. --- ## 🎯 The .FAF Position ``` Model Context Protocol ───── ─────── ──────── Claude → .faf → MCP Gemini → .faf → MCP Codex → .faf → MCP Any LLM → .faf → MCP ``` **.FAF is the foundational layer.** Universal context format. IANA-registered (`application/vnd.faf+yaml`). Works with any AI. --- ## 📦 Ecosystem - **[faf-cli](ht ---

FAQ

What is the FAF (File & Project Context) MCP server?
FAF (File & Project Context) is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for FAF (File & Project Context)?
This profile displays 48 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.648 reviews
  • Carlos Khan· Dec 24, 2024

    FAF (File & Project Context) has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Chaitanya Patil· Dec 16, 2024

    FAF (File & Project Context) is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Ira Sethi· Dec 16, 2024

    FAF (File & Project Context) reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Aisha Desai· Dec 12, 2024

    FAF (File & Project Context) is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Neel Mehta· Nov 15, 2024

    We evaluated FAF (File & Project Context) against two servers with overlapping tools; this profile had the clearer scope statement.

  • Piyush G· Nov 7, 2024

    Useful MCP listing: FAF (File & Project Context) is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Mateo Bhatia· Nov 7, 2024

    We wired FAF (File & Project Context) into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Ira Reddy· Nov 3, 2024

    Useful MCP listing: FAF (File & Project Context) is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Shikha Mishra· Oct 26, 2024

    FAF (File & Project Context) reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Diya Gill· Oct 26, 2024

    FAF (File & Project Context) is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

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