gemini-faf-mcp — The Full-Facts Edition

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

FAF defines. MD instructs. AI codes.

⭐ A star helps other devs discover gemini-faf-mcp — despite the downloads, ~3 of 4 devs check stars first.

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: [writes 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 v2.8.0 — The Full-Facts Edition

faf_auto now grounds its detection in the repo's own files, and every faf_model reference template scores 100% Trophy.

faf_auto used to read only the root manifest (pyproject.toml, package.json, …). It now also reads the files that carry the real stack: docker-compose service images map onto database / cache / search / storage (a running Postgres service is the database — it beats a dependency guess), Makefile / justfile targets map onto the commands block (test / build / check-all, root file or a nested backend/Makefile), and .github/workflows/ sets cicd. A polyglot repo that reported library / JavaScript now reports its real Postgres + Redis + FastAPI stack. In parity with faf-cli 7.10.

Separately: the 15 faf_model reference templates were scoring 48–57% — they filled 4 stack slots and used null. All rewritten to the full 21-slot schema; every one is 100% Trophy now, and a test keeps it that way. 13 tools · 262 tests.

v2.7.1 / v2.7.0 — The Interop Edition — faf_agents / faf_gemini rewritten as faf-python-sdk authoring-tool wrappers (were 4-field stubs); new faf_migrate brings a .faf up to the current format. v2.6.0 — The Agent Card Edition added a real agent.fafa passport, an MCP Server Card (SEP-2127), and an AI Catalog entry. v2.5.0 — The Dart Edition detects Dart/Flutter from pubspec.yaml. v2.4.2 — The Confinement Edition confined every caller path argument. v2.4.0 — The Chameleon Edition auto-selects its transport: stdio locally, Streamable HTTP on Cloud Run.

One-Minute Setup

1. Install

uvx gemini-faf-mcp          # zero-install run via uvx (fetched from PyPI)
# or: pip3 install gemini-faf-mcp

2. Add to Gemini CLI

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

3. Author your project context

In your Gemini CLI:

> /faf: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 /faf:score to see what's missing and how to push to 100% (TROPHY).

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: "3.0"
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 authors it. Run faf_gemini and you get a fresh GEMINI.md in Gemini CLI's own hierarchical, @file-importable convention — setup, verify, key files, stack, confirm-first actions — authored from a single source of truth instead of hand-maintained. Your hand-written content outside the faf-managed block is preserved.

> /faf:export
# Authors 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.

Auto-Detect Your Stack

faf_auto scans your project's manifest files and its docker-compose services, Makefile targets, and CI config, then authors a .faf with accurate slot values. No manual entry needed.

> Auto-detect my project stack

{
  "detected": {
    "main_language": "Python",
    "package_manager": "uv",
    "framework": "FastAPI",
    "api_type": "REST",
    "database": "PostgreSQL",
    "cache": "Redis",
    "hosting": "Docker Compose",
    "cicd": "GitHub Actions",
    "commands": { "test": "make test", "build": "make build", "lint": "make check-all" }
  },
  "score": 79,
  "tier": "GREEN"
}

database and cache came from docker-compose.yml, commands from the Makefile, cicd from .github/workflows/ — none of which the manifest scan sees. Fill in the six W's and you are at Trophy.

What it scans:

Priority rule: pyproject.toml / Cargo.toml / go.mod take priority over package.json. File-facts (a real compose service, a Makefile target) win over dependency guesses. Only sets values that are actually detected — no hardcoded defaults.

All 13 Tools

Create & Detect

Validate & Score

Read & Transform

Export & Interop

Migrate

Reference

Score and Tier System

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