gemini-faf-mcp

Persistent Project Context for Google Gemini. Define once. Sync everywhere.

FAF defines. MD instructs. AI codes.

Stop re-explaining your project to every new Gemini session. Every Gemini conversation starts cold — you re-state your stack, your goals, your conventions every single time. .faf is one structured file that captures all of it. This package is the MCP server that lets Gemini read it.

Before and after

Without FAF                           With FAF (.faf at 85%+ Bronze)
─────────────────────────             ─────────────────────────
You: "I'm using FastAPI with...       You: "Add a /users/me endpoint"
      PostgreSQL, pytest, and..."     Gemini: [generates correct code,
Gemini: "Got it. What's the              uses your auth pattern,
        codebase like?"                  matches your test style]
You: "It's a REST API for..."
[5 minutes of re-explaining]
Gemini: [now ready to help]

.faf is read once at session start. Every tool call lands on a Gemini that already knows your project.

What's New in v2.2.0

Mk4 Championship Scoring Engine — all 12 tools now use the same scoring algorithm as the Rust compiler and TypeScript CLI. faf_score and faf_validate return slot-level detail (populated, active, total). Scores match across every FAF tool in every language. 221 tests, 41 new WJTTC championship tests. Dead code removed (sync_faf.py).

v2.2.1 is a patch release — package description aligned with the canonical "Persistent project context for Google Gemini" wording across PyPI, the Gemini Extensions Gallery, and the MCP Registry. No code changes.

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One-Minute Setup

1. Install

pip install gemini-faf-mcp

2. Add to Gemini CLI

gemini extensions install https://github.com/Wolfe-Jam/gemini-faf-mcp

3. Generate your project context

In your Gemini CLI:

> /gemini-faf-mcp:setup

You should see: Created project.faf — Score: 85% (BRONZE). From this point, every Gemini session in this project reads it automatically.

Tip: A score of 85% (BRONZE) is the minimum where Gemini stops guessing. Run /gemini-faf-mcp:score to see what's missing and how to push to 100% (TROPHY).

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The "One-File" Advantage

A .faf file is structured YAML that captures your project DNA. Every AI agent reads it once and knows exactly what you're building.

# project.faf — your project, machine-readable
faf_version: '2.5.0'
project:
  name: my-api
  goal: REST API for user management
  main_language: Python
stack:
  backend: FastAPI
  database: PostgreSQL
  testing: pytest
human_context:
  who: Backend developers
  what: User CRUD with auth
  why: Replace legacy PHP service

Result: Gemini reads this once and knows your project. No 20-minute onboarding. No wrong assumptions. Every session starts aligned.

FAF defines. MD instructs. AI codes.

What about my GEMINI.md?

You don't replace it. .faf generates it. Run faf_gemini and you get a fresh GEMINI.md with the structured project data baked in as YAML frontmatter — the same GEMINI.md Gemini CLI already reads, but generated from a single source of truth instead of hand-maintained.

> /gemini-faf-mcp:export
# Generates GEMINI.md from project.faf

.faf is the source. GEMINI.md is one of its outputs. Same logic for AGENTS.md (OpenAI Codex), .cursorrules, CLAUDE.md, and others — write once, render everywhere.

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Auto-Detect Your Stack

faf_auto scans your project's manifest files and generates a .faf with accurate slot values. No manual entry needed.

> Auto-detect my project stack

{
  "detected": {
    "main_language": "Python",
    "package_manager": "pip",
    "build_tool": "setuptools",
    "framework": "FastMCP",
    "api_type": "MCP",
    "database": "BigQuery"
  },
  "score": 100,
  "tier": "TROPHY"
}

What it scans:

File	Detects
pyproject.toml	Python + build system + frameworks (FastAPI, Django, Flask, FastMCP) + databases
package.json	JavaScript/TypeScript + frameworks (React, Vue, Next.js, Express)
Cargo.toml	Rust + cargo + frameworks (Axum, Actix)
go.mod	Go + go modules + frameworks (Gin, Echo)
requirements.txt	Python (fallback)
Gemfile	Ruby
composer.json	PHP

Priority rule: pyproject.toml / Cargo.toml / go.mod take priority over package.json. Only sets values that are actually detected — no hardcoded defaults.

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All 12 Tools

Create & Detect

Tool	What it does
faf_init	Create a starter .faf file with project name, goal, and language
faf_auto	Auto-detect stack from manifest files and generate/update .faf
faf_discover	Find .faf files in the project tree

Validate & Score

Tool	What it does
faf_validate	Full Mk4 validation — score, tier, slot counts, errors, warnings
faf_score	Quick Mk4 score — score, tier, populated/active/total slot counts

Read & Transform

Tool	What it does
faf_read	Parse a .faf file into structured data
faf_stringify	Convert parsed FAF data back to clean YAML
faf_context	Get Gemini-optimized context (project + stack + score)

Export & Interop

Tool	What it does
faf_gemini	Export GEMINI.md with YAML frontmatter for Gemini CLI
faf_agents	Export AGENTS.md for OpenAI Codex, Cursor, and other AI tools

Reference

Tool	What it does
faf_about	FAF format info — IANA registration, version, ecosystem
faf_model	Get a 100% Trophy-scored example .faf for any of 15 project types

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Score and Tier System

Your .faf file is scored on completeness — how many slots are filled with real values.

Score	Tier	Meaning
100%	TROPHY	AI has full context for your project
99%	GOLD	Exceptional
95%	SILVER	Top tier
85%	BRONZE	Minimum recommended — AI can build from here
70%	GREEN	Solid foundation
55%	YELLOW	Needs improvement
<55%	RED	Major gaps — AI will guess
0%	WHITE	Empty

Aim for Bronze (85%+). That's where AI stops guessing and starts knowing.

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Using with Gemini CLI

> Create a .faf file for my Python FastAPI project
> Auto-detect my project and fill in the stack
> Score my .faf and show what's missing
> Export GEMINI.md for this project
> Show me a 100% example for an MCP server
> What is FAF and how does it work?
> Read my project.faf and summarize the stack
> Validate my .faf and fix the warnings

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Architecture

gemini-faf-mcp v2.2.1
├── server.py              → FastMCP MCP server (12 tools, Mk4 scoring)
├── main.py                → Cloud Run REST API (GET/POST/PUT)
├── models.py              → 15 project type examples
└── src/gemini_faf_mcp/    → Python SDK (FAFClient, parser)

The MCP server delegates to faf-python-sdk for parsing, validation, and Mk4 scoring. Stack detection in faf_auto is Python-native — no external CLI dependencies.

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Testing

pip install -e ".[dev]"
python -m pytest tests/ -v

221 tests passing across 9 WJTTC tiers (125 MCP server + 55 Cloud Function + 41 Mk4 WJTTC championship). Championship-grade test coverage — WJTTC certified.

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FAF Ecosystem

One format, every AI platform.

Package	Platform	Registry
claude-faf-mcp	Anthropic	npm + MCP #2759
gemini-faf-mcp	Google	PyPI
grok-faf-mcp	xAI	npm
rust-faf-mcp	Rust	crates.io
faf-cli	Universal	npm

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Python SDK

Use FAF directly in Python without MCP:

from gemini_faf_mcp import FAFClient, parse_faf, validate_faf, find_faf_file

# Parse and validate locally
data = parse_faf("project.faf")
result = validate_faf(data)
print(f"Score: {result['score']}%, Tier: {result['tier']}")

# Find .faf files automatically
faf_path = find_faf_file(".")

# Or use the Cloud Run endpoint
client = FAFClient()
dna = client.get_project_dna()

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Cloud Run REST API

Live endpoint for badges, multi-agent context brokering, and voice-to-FAF mutations.

https://faf-source-of-truth-631316210911.us-east1.run.app

Supports agent-optimized responses (Gemini, Claude, Grok, Jules, Codex/Copilot/Cursor) via X-FAF-Agent header. Voice mutations via Gemini Live through PUT endpoint. Auto-deploys via Cloud Build on push to main.

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

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Links

• FAF Specification
• IANA Registration
• faf-python-sdk
• faf-cli
• Changelog

License

MIT

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Built by @wolfe_jam | wolfejam.dev

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Get the CLI

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

npx faf-cli auto

Anthropic MCP #2759 · IANA Registered: application/vnd.faf+yaml · faf.one · npm