<!-- faf: rust-faf-mcp | Rust | mcp-server | RMCP — the Rust-native MCP server for FAF (Foundational AI-context Format). Single binary, stdio transport, 4.3 MB stripped. cargo install rust-faf-mcp. Built on the rmcp Rust MCP SDK + faf-rust-sdk. --> <!-- mcp-name: io.github.Wolfe-Jam/rust-faf-mcp -->

rust-faf-mcp

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

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.

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

cargo install 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

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

Score & Validate

Optimize

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 for parsing, validation, compression, discovery
• Server: rmcp 1.1 with #[tool_router] macro — handles JSON-RPC, schema generation, transport

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

Testing

112 tests across 6 files:

cargo test    # runs all 112

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.

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.

Links

• crates.io/crates/rust-faf-mcp
• faf-rust-sdk — the parser this depends on
• faf.one — FAF home
• IANA registration — application/vnd.faf+yaml
• MCP Registry name: mcp-name: io.github.Wolfe-Jam/rust-faf-mcp
• CHANGELOG

License

MIT

Built by @wolfe_jam | wolfejam.dev