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rust-faf-mcp

Persistent Project Context for Rust MCP clients. Native. Fast. cargo install

The one.faf Edition (v0.4.3)one.faf/rust-faf-mcp · rmcp 3.0.1 (MCP Tier 1 foundation) · solid cargo-native Rust MCP for Rust devs

v0.4.3 — npm Trusted Publishing (OIDC) fixed — the dummy NODE_AUTH_TOKEN setup-node injected was 404ing the OIDC token exchange; CI now publishes npm clean.

FAF defines. MD instructs. AI codes.

Stop re-explaining your project to every AI session. One .faf file holds your persistent project context. Every AI reads it once and knows what you're building.

Crates.io FAF Trophy 100% Tests IANA License

Rust-native MCP (Model Context Protocol) server for FAF — structured AI project context in YAML (application/vnd.faf+yaml). Single binary, stdio transport, 4.3 MB stripped. Built on rmcp and faf-rust-sdk.

Quickstart

# Rust toolchain:
cargo install rust-faf-mcp

# No Rust (downloads GH Release binary for darwin/linux x64):
npx rust-faf-mcp

Then point any MCP client at it:

# Claude Code
claude mcp add faf rust-faf-mcp
// WARP / Cursor / Zed / Claude Desktop — any stdio MCP client
{
  "mcpServers": {
    "faf": {
      "command": "rust-faf-mcp"
    }
  }
}

No flags, no config files, no network listener. Pure stdio JSON-RPC.

Or via Homebrew (macOS, pre-built):

brew install Wolfe-Jam/faf/rust-faf-mcp

Related MCP server: gemini-faf-mcp

One command, done forever

faf_auto detects your project, creates a .faf, enhances it to max score, and syncs CLAUDE.md — in one shot:

faf_auto complete
━━━━━━━━━━━━━━━━━
Score: 0% → 85% (+85) ◇ BRONZE
Steps:
  1. Created project.faf
  2. Second enhancement pass
  3. Created CLAUDE.md

Path: /home/user/my-project

What it produces:

# project.faf — your project, machine-readable
faf_version: "3.3"
project:
  name: my-api
  goal: REST API for user management
  main_language: Rust
  version: "0.1.0"
  license: MIT
instant_context:
  what_building: REST API for user management
  tech_stack: Rust 2024
  key_files:
    - Cargo.toml
    - src/main.rs
    - README.md
  commands:
    build: cargo build
    test: cargo test
stack:
  backend: Rust
  build_tool: cargo

Every AI agent reads this once and knows exactly what you're building. No 20-minute onboarding. No wrong assumptions.

Tools

Create & Detect

Tool

What it does

faf_auto

Zero to AI context in one command — init, enhance, sync, score, done

faf_init

Create or enhance project.faf from Cargo.toml, package.json, pyproject.toml, or go.mod

faf_git

Generate project.faf from any GitHub repo URL — no clone needed

faf_discover

Walk up the directory tree to find the nearest project.faf

Score & Validate

Tool

What it does

faf_score

Score AI-readiness 0-100% with field-level breakdown

faf_sync

Sync project.fafCLAUDE.md (preserves existing content)

Optimize

Tool

What it does

faf_read

Parse and display project.faf contents

faf_compress

Compress .faf for token-limited contexts (minimal / standard / full)

faf_tokens

Estimate token count at each compression level

faf_init is iterative — run it again and it fills in what's missing. Score goes up each time.

Architecture

src/
├── main.rs      # ~20 lines — tokio entry, rmcp stdio transport
├── server.rs    # FafServer: #[tool_router], ServerHandler, resources
└── tools.rs     # Business logic — all 9 tools, pure functions returning Value
  • Runtime: tokio single-threaded (current_thread)

  • HTTP: reqwest async (only used by faf_git for GitHub API)

  • SDK: faf-rust-sdk 1.3 (Cargo pin — parse / validate / compress / discover; foundation 3.x lives in faf-rust)

  • Server: rmcp 3.0.1 with #[tool_router] / #[tool_handler] — JSON-RPC, schema generation, stdio transport (Tier-1 assessed SDK cut)

Tools return serde_json::Value. The server adapts them to Result<String, String> for rmcp's IntoCallToolResult.

Testing

117 tests (113 integration + 4 unit):

cargo test    # runs all 117

# Full ship bar (same gates as GitHub CI — run before push)
bash scripts/ci.sh
# Optional: block push on red CI twin
bash scripts/install-hooks.sh

File

Tests

Coverage

mcp_protocol.rs

9

Init handshake, tools/list, resources, schema validation, ID preservation

tools_functional.rs

25

All 9 tools — happy path, error paths, language detection

tier1_security.rs

12

Path traversal, null bytes, shell injection, oversized input, malformed JSON

tier2_engine.rs

35

Corrupt YAML, sync replacement, pipelines, dual manifests, legacy filenames, direct paths

tier3_edge_cases.rs

10

Unicode, CJK, score boundaries, unknown fields, GitHub URL parsing

tier4_aero.rs

22

Manifest structure, version sync, server.json, context block, manifest-server cross-validation

src unit

4

Skills extension digest + scoring resource

Tests spawn the compiled binary as a subprocess and communicate via stdin/stdout JSON-RPC — true integration tests against the real server.

FAF Ecosystem

One format, every AI platform.

Package

Platform

Registry

rust-faf-mcp

Rust

crates.io

claude-faf-mcp

Anthropic

npm + MCP #2759

gemini-faf-mcp

Google

PyPI

grok-faf-mcp

xAI

npm

faf-cli

Universal

npm

Build from source

git clone https://github.com/Wolfe-Jam/rust-faf-mcp
cd rust-faf-mcp
cargo build --release
# Binary at target/release/rust-faf-mcp (4.3 MB)

Edition: 2024 | LTO: enabled | Strip: symbols

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

Citation

If you use rust-faf-mcp or the .faf / .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). Why Agents Need a Passport: .fafa — Portable Identity for the Agentic Era. Zenodo. https://doi.org/10.5281/zenodo.21951641

License

MIT


Built by @wolfe_jam | wolfejam.dev

A
license - permissive license
Not graded
quality - not tested
A
maintenance

Maintenance

Maintainers
Response time
3wRelease cycle
10Releases (12mo)
Commit activity

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