Skip to main content
Glama

grok-faf-mcp v2.1 — The New Era Edition

IANA: vnd.faf+yaml DOI: Context paper DOI: Agents paper

Home: faf.one/grok Live demo: grok.faf.one

⭐ Star faf-cli on GitHub, the CLI this server is built on: bookmarks it for you, helps other devs find it too.

grok-faf-mcp hero

npm version Smithery FAF Trophy 100% CI License: MIT project.faf Chat to FAFA live

Stars Downloads Business card · Get yours →

FAF defines. AGENTS.md instructs. AI codes.

v2.2.4 — The Instructions Edition

faf_sync keeps your hand-written files, writes the context files it names, and tells you what it couldn't write. Managed blocks now go through faf-cli's injector, so a hand-written AGENTS.md or CLAUDE.md is never reclaimed; faf_sync writes the context files it names, one file at a time, and the reply says what it couldn't write and why.

v2.2.3

faf_write can no longer overwrite the birth certificate or your context files. .faf-dna is refused in every spelling and route, existing project.faf, CLAUDE.md and AGENTS.md need overwrite: true, and writes stay inside the project folder.

v2.2.2

Cleanup: removes leftover two-way sync wording. Two-way sync was dropped by design long ago: project.faf pushes structured data out, and prose doesn't come back. The last strings that still said two-way now match. faf_bi_sync still works as the old name for faf_sync.

v2.2.1

One rule for project questions: read project.faf first. New line 2 of the server instructions: "For questions about the project (stack, goal, commands), read project.faf first and answer from it." The same rule in faf-mcp moved Grok Build from 0 of 3 to 2 of 3 runs reading project.faf first, on a repo where project.faf and package.json disagree (3-run pilot). 715 characters.

v2.2.0

grok-faf-mcp now tells Grok how its tools fit together, context and memory: server instructions in initialize (615 characters), plus faf-cli 8.5.0, which knows a skills repo when it sees one.

  • Server instructions. When Grok connects, the server says how its tools fit together: context (project.faf) and memory (.fafm), which tool checks, creates or syncs, the refresh tools, and that rag_query needs an xAI Collection.

  • faf-cli 8.5.0 inside. faf_init detects a skills repo: SKILL.md folders make it skills (10 slots), and a Claude Code plugin manifest makes it skills-plugin (14).

  • Advisories cleared in the MCP SDK, proxy-addr and hono.

Earlier: v2.1.0 — The New Era Edition — glass-box tools: every tool says if it reads, writes or rewrites; its own FAF passport at grok.faf.one; a new look.

Earlier: v2.0.0 — The Always33 Edition — one engine, one number: every score is faf-cli 8's always-33 kernel, on npm and on mcpaas.live. ZEPH is opt-in until the Zig engine is always-33.

☆ Bookmark grok.faf.one for later.

First v0.2-conformant reader of the FAF Context Ingestion Contract — the open standard co-authored in public with @grok.


Related MCP server: claude-faf-mcp

Install — one line

Add to ~/.grok/config.toml:

[mcp_servers.grok-faf-mcp]
url = "https://mcpaas.live/grok/mcp/v1"

Restart Grok TUI (or /mcps r) to refresh. The hosted endpoint scores always-33 — the same number as the npm package. Tools: faf_score, faf_validate, faf_get_tier, faf_estimate_tokens, faf_analyze (plus soul/memory ops).

Smithery: wolfe-jam/grok-faf-mcp — gateway at https://grok-faf-mcp--wolfe-jam.run.tools

Homebrew (local stdio):

brew install wolfe-jam/faf/grok-faf-mcp

Hosted on Cloudflare Workers — sub-ms cold start, no subprocess, edge-served. Scores and validates with the faf-kernel WASM (always-33, the same engine as the npm package) since mcpaas-cf 1.8.1; a Zig WASM engine handles token estimates and tier. Externally validated by Grok S1 + S2 on 2026-05-27.

Verify the live contract:

curl https://mcpaas.live/grok/mcp/v1/info

Returns endpoint, protocol versions, engine details, tool list, and the architecture line: .faf=vROM | AI-in-session=RAM.

Sample corpus: xai-faf-proof/pilot — 10 records ready to score.


The 6 Ws - Quick Reference

Every README should answer these questions. Here's ours:

Question

Answer

WHO is this for?

Grok/xAI developers and teams building with URL-based MCP

WHAT is it?

Persistent project context for xAI Grok — URL-first deployment, IANA-registered .faf format

WHERE does it work?

Cloudflare Workers (mcpaas.live/grok/mcp/v1) • Any MCP client supporting native url= config • Self-deploy to your own CF/Vercel worker

WHY do you need it?

Zero-config MCP on a URL — Grok asked for it, we built it first

WHEN should you use it?

Grok integration, xAI projects, any url-based MCP client

HOW does it work?

url = "https://mcpaas.live/grok/mcp/v1" — context tools served from edge via MCPaaS (sub-ms cold start, no subprocess)

For AI: Read the detailed sections below for full context. For humans: Use this pattern in YOUR README. Answer these 6 questions clearly.

For the xAI / Grok Build team

Built for Grok and shaped by direct Grok feedback.
Open for native Grok Build integration, .fafm memory layer, refresh_faf primitives, or any other context features the team needs.
Live and dogfooded at https://grok.faf.one and https://mcpaas.live/grok/mcp/v1.

Context for Grok agents: faf-cli authors what Grok agents read from real project detection — bunx faf export --agents. faf-cli's src/interop/grok.ts wires this MCP into .grok/config.toml (that file lives in the faf-cli repo, not here). See FAF-CLI for Grok & xAI agents.


The Problem

Every Grok session starts from zero. You re-explain your stack, your goals, your architecture. Every time.

.faf fixes that. One file, your project DNA, persistent across every session.

Without .faf  →  "I'm building a REST API in Rust with Axum and PostgreSQL..."
With .faf     →  Grok already knows. Every session. Forever.

One Command, Done Forever

faf_auto detects your project, creates a .faf, and scores it — in one shot:

faf_auto
━━━━━━━━━━━━━━━━━
Score: 0% → 85% (+85) ◇ BRONZE
Steps:
  1. Created project.faf
  2. Detected stack from package.json
  3. Synced 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: TypeScript
stack:
  backend: Express
  database: PostgreSQL
  testing: Jest
  runtime: Node.js
human_context:
  who: Backend developers
  what: User CRUD with auth
  why: Replace legacy PHP service

Every AI agent reads this once and knows exactly what you're building.


⚡ What You Get

URL:     https://mcpaas.live/grok/mcp/v1
Format:  IANA-registered .faf (application/vnd.faf+yaml)
Tools:   12 core by default (bunx) — re-grounding (refresh_faf/fafm/blend), LAZY-RAG, orchestration substrate, FAF essentials · extended utilities via FAF_TOOLS=all · 19 hosted (WASM-pure, served by mcpaas-cf) on the URL
Engine:  faf-cli 8 — faf-scoring-kernel 3.0.0 (Rust → WASM, always-33)
Speed:   0.5ms average (was 19ms — 3,800% faster with Mk4)
Tests: WJTTC parity (heavy local ↔ light hosted) + full suites. Runner: sh scripts/run-tests.sh (bun + flake retry)
Status:  FAST⚡️AF

MCP on a URL. Point your Grok integration at the URL. That's it.


Scoring: From Blind to Optimized

Tier

Score

What it means

🏆 TROPHY

100%

Gold Code — AI is optimized

★ GOLD

99%+

Near-perfect context

◆ SILVER

95%+

Excellent

◇ BRONZE

85%+

Strong baseline

● GREEN

70%+

Solid foundation

● YELLOW

55%+

AI flipping coins

○ RED

<55%

AI working blind

♡ WHITE

0%

Start — good luck

At 55%, Grok guesses half the time. At 100%, Grok knows your project.


Two Ways to Deploy

Point your MCP client at the production URL — edge-served on Cloudflare Workers, no subprocess, sub-ms cold start. WASM-pure tools only on this path (scoring, validation, refresh_faf).

{
  "mcpServers": {
    "grok-faf": {
      "url": "https://mcpaas.live/grok/mcp/v1"
    }
  }
}

2. Local (stdio — for FS-touching workflows)

Use the local stdio path when you need filesystem access (faf_init, faf_sync, file-mutating tools):

brew install wolfe-jam/faf/grok-faf-mcp   # macOS tap
# or
bunx grok-faf-mcp

Or via MCP config:

{
  "mcpServers": {
    "grok-faf": {
      "command": "bunx",
      "args": ["grok-faf-mcp"]
    }
  }
}

MCP Tools

Create & Detect

Tool

Purpose

faf_init

Create project.faf from your project

faf_auto

Auto-detect stack and populate context

faf_score

AI-readiness score (0-100%) with breakdown

faf_status

Check current AI-readability

refresh_faf

Re-ground on the live .faf — re-read + re-score, report drift, return fresh DNA (drift → refresh → re-grounded). Requested by Grok.

Drift & Orchestration (1.5 — the prestige release)

Tool

Purpose

refresh_fafm

Re-ground on the live .fafm memory layer for one or more souls. Returns a stamped delta (added/updated facts) by default; verbatim: true for full content. Read-only · always stamped. Sister to refresh_faf for the RAM/memory layer in the vROM/RAM model. Built for Grok, by request.

refresh_blend

The baked-in two-intensity refresh (Cmd+R / Cmd+Shift+R analog). mode: "blend" (default) fires refresh_faf (light) + refresh_fafm (delta); mode: "nuke" fires both at hard intensity. Blend is BAKED IN, NOT a dial — both layers always fire; mode only affects fafm intensity.

faf_orchestrate_recommendation

The heavy orchestrator. Reads current substrate state, composes the full 1.5 library substrate (drift detection · CheckID · repeat-offender · take-a-hint · refresh history), returns a structured Recommendation with recommend, severity, summary, reason, and a rich hints object including effective_policy (the tier in force). Advisory only — never auto-fires (subordinate-not-daemon). Writes a recommendation receipt on every call (no silent decisions). Spec source: Grok-1 FAF-DRIFT-DETECTION-SPEC §9.5 + Appendix C.

faf_get_orchestration_policy

Pure introspection of the effective policy WITHOUT running the orchestrator. Returns { tier, thresholds, source, overrides_applied } — what aggressiveness tier the next orchestration call would use, and whether it came from defaults or a .faf:orchestration: override. No drift detection · no signals · no receipt write — the quietest tool in the 1.5 substrate. Useful for debugging unexpected orchestrator behavior, pre-flight checks before bulk operations, and override-took-effect verification.

Sync & Persist

Tool

Purpose

faf_sync

Write the context files from project.faf: the ones that exist (CLAUDE.md, .cursorrules, .windsurfrules, .clinerules), or CLAUDE.md when there are none; target: all writes every one. One way: it never writes project.faf. A file faf can't safely write is left unchanged and named in the reply. (faf_bi_sync, the old name, still works.)

faf_trust

Validate .faf integrity

Read & Write

Tool

Purpose

faf_read

Read any file

faf_write

Write a file inside the project. Never writes .faf-dna; replacing an existing *.faf, CLAUDE.md or AGENTS.md needs overwrite: true

faf_list

Discover projects with .faf files

RAG & Grok-Exclusive

Tool

Purpose

rag_query

RAG-powered context retrieval

rag_cache_stats

RAG cache statistics

rag_cache_clear

Clear RAG cache

grok_go_fast_af

Auto-load .faf context for Grok

Plus 34 advanced tools available with FAF_SHOW_ADVANCED=true.


Performance

Execution:    0.5ms average (97% faster than v1.1)
Fastest:      3,360ns (version — nanosecond territory)
Slowest:      1.3ms (score — Mk4 WASM)
Improvement:  19ms → 0.5ms (3,800% faster)
Engine:       faf-cli 8 (faf-scoring-kernel 3.0.0, always-33)
Memory:       Zero leaks
Transport:    stdio (local, bunx) · Streamable HTTP (hosted, Cloudflare Workers)

Benchmarked 10x per tool, warmed up, on local stdio execution. Hosted edge adds sub-ms cold start on top.

Orchestrator (faf_orchestrate_recommendation) characteristics: composition call — reads up to 6 files (.faf, .fafm, package.json, CHANGELOG.md, README.md, plus all 3 receipt logs), runs 2 analyzers (detectFafmDrift + checkId), evaluates the decision table, writes 1 receipt. Expected latency: tens of ms on warm cache; higher under cold-disk or very large .fafm corpora. Designed for occasional agent-initiated calls, not per-turn polling. detectFafmDrift is O(n²) in fact count (cross-fact n-gram recurrence) — comfortable up to ~hundreds of facts.


Architecture

grok-faf-mcp
├── src/
│   ├── server.ts             → MCP server (GrokFafMcpServer)
│   ├── handlers/
│   │   ├── championship-tools.ts  → 55+ tool definitions
│   │   ├── tool-registry.ts       → Visibility filtering (core/advanced)
│   │   └── engine-adapter.ts      → FAF engine bridge
│   ├── faf-core/commands/score.ts → faf-cli scoreFafYaml (always-33)
│   ├── types/                     → Canonical type substrate (1.5)
│   │   ├── drift-signals.ts       → DriftSignal · Contradiction · RepeatOffender
│   │   ├── refresh.ts             → RefreshMode
│   │   ├── escalation.ts          → EscalationLevel
│   │   ├── recommendation.ts      → RecommendationAction
│   │   └── receipts.ts            → ReceiptMetadata
│   ├── detection/fafm-drift.ts    → detectFafmDrift() — repetition-rate gauge
│   ├── integrity/check-id.ts      → checkId() — cross-stamp contradiction check
│   ├── orchestrator/
│   │   ├── repeat-offender.ts     → RepeatOffenderTracker
│   │   ├── take-a-hint.ts         → evaluateTakeAHint() — escalation ladder
│   │   ├── refresh-blend.ts       → runRefreshBlend()
│   │   └── recommendation.ts      → analyzeAndRecommend() + orchestrate()
│   └── telemetry/
│       ├── refresh-receipts.ts        → RefreshReceiptsLog
│       └── recommendation-receipts.ts → RecommendationReceiptsLog
├── smithery.yaml             → Smithery listing config
├── api/index.ts              → Vercel catch-site (legacy showcase surface; kept alive)
└── vercel.json               → Vercel routing for the catch-site

Production deployment: Cloudflare Workers via mcpaas-cf (serving mcpaas.live/grok/mcp/v1). The api/index.ts + vercel.json paths above stay alive as a catch-site for legacy/bookmarked links — they are no longer the production path.

Scoring pipeline: faf-cli's scoreFafYaml → the always-33 kernel (faf-scoring-kernel, Rust → WASM) scores the file as written: 33 slots, slotignored slots drop out of the denominator. No per-type rewriting, no fallback scorer — the same number faf-cli, claude-faf-mcp and faf-mcp give.


Testing

WJTTC parity (heavy local ↔ light hosted) + full suites (green on CI):

sh scripts/run-tests.sh

Suite

Coverage

desktop-native-validation

Core native functions, security, performance

mcp-conformance

MCP protocol conformance — tools, transport, errors

wjttc-mcp

WJTTC MCP certification

wjttc-bun

WJTTC bun-migration + integrity

rag-system

RAG query, caching, context retrieval

security

Input validation + security guards

visibility

Tool visibility (core/advanced filtering)


Status & known limitations (v2.2)

v2.2.0 — The Instructions Edition — server instructions in initialize (615 characters) and faf-cli 8.5.0, which detects skills repos. Scoring is unchanged from v2.0.0.

v2.1.0 — The New Era Edition — glass-box tools (every tool labelled read, write or rewrite), its own FAF passport at grok.faf.one, a new look. Scoring is unchanged from v2.0.0.

v2.0.0 — The Always33 Edition — one engine, one number: faf-cli 8's always-33 kernel on npm and on mcpaas.live. ZEPH is opt-in until the Zig engine is always-33. FafCompiler upgraded to the Always33 fafb model — parity across frontier models, Enterprise/Teams ready.

Everything below still applies; operating it honestly means surfacing what's NOT in here alongside what is.

Earlier: v1.10.0 — The No-Fluff Edition — no fluff in a project.faf. faf_enhance is gone. RAG default is grok-4.6. Fill stays on faf_auto / faf_go.

Earlier: v1.9.0 — The ZEPH Default Edition — the proven-fast Zig→WASM scoring path behind refresh_faf is now default-ON (same score, cheaper to compute; parity proven byte-identical — CI gate + 91/91 live). Kill switch USE_ZEPH=0 forces the canonical scorer. FRC tools stay opt-in behind USE_FRC.

Earlier: v1.8.0 — The Closed-Loop Edition — observability writes, token math is honest, FRC contract locked. The drift→refresh→re-ground loop can finally be measured. Earlier: v1.7.0 — The Grounded Memory Edition — ZEPH + the FRC layer over Grok Collections (faf_gate/faf_section/faf_memory), opt-in via USE_FRC/USE_ZEPH; 12-tool core unchanged. Earlier: v1.6.0 — The ZEPH Edition — the ZEPH fast path for re-grounding (refresh_faf/refresh_blend via Zig→WASM cascade.wasm, ~12µs, USE_ZEPH=1; faf-cli stays canonical, parity locked in CI).

What is fully supported:

  • 19 tools on the hosted endpoint (https://mcpaas.live/grok/mcp/v1 and client-specific routes) — scoring · validation · refresh_faf · a content-in faf_orchestrate_recommendation · the FRC trio · souls and search. The live list is at …/grok/mcp/v1/info.

  • refresh_faf and refresh_fafm as explicit, callable re-grounding primitives.

  • refresh_blend as the baked-in two-intensity refresh (Cmd+R / Cmd+Shift+R analog).

  • faf_orchestrate_recommendation — the heavy orchestrator that composes drift signals, recurrence, receipts, and take-a-hint into an advisory recommendation.

  • faf_get_orchestration_policy — pure introspection of the effective policy without running the orchestrator (no drift detection, no receipt write — the quietest tool in the substrate).

  • Full policy visibility (effective_policy) returned on every orchestration call AND surfaced standalone via faf_get_orchestration_policy.

Current limitations:

  • faf_get_orchestration_policy, refresh_fafm, refresh_blend, faf_init and faf_sync need filesystem access and run only on the local stdio path (bunx grok-faf-mcp / npx grok-faf-mcp). The hosted faf_orchestrate_recommendation takes file contents instead of reading your repo, and has no receipts or recurrence history (it reports those under partial[]).

  • Receipt storage — cwd-relative JSON, pull-discoverable. Three append-only JSON files live at the repo root with stable schemas:

    .faf-drift-index.json              ← RepeatOffenderTracker — per-slot recurrence counts
    .faf-refresh-receipts.json         ← RefreshReceiptsLog    — every refresh fire
    .faf-recommendation-receipts.json  ← RecommendationReceiptsLog — every orchestrator call

    Pull-discoverable by external tools (TAF, custom indexers, observability dashboards) — read on your own schedule, no callback/push API required. Promotion to a dedicated orphan branch (mirroring the TAF pattern) is documented but deferred per ship discipline; the cwd-relative JSON is the v1 bootstrap.

  • No multi-process file lock on the receipt logs. Within a process, the JS event loop serializes writes. Multi-agent concurrent writes can race; future task.

  • Aggressiveness tier hook — .faf:orchestration:tier reads 'conservative' (default — quietest, no noisy first-impression) · 'balanced' · 'aggressive'. active_tier always surfaced in hints.effective_policy for observability, and standalone via faf_get_orchestration_policy. The policy WRITER (faf_set_orchestration_policy) and scheduling (faf_schedule_heavy_re_ground) are not included in v1.5 — edit .faf:orchestration:tier: directly to override.

  • No ack mechanism yet for recommendation receipts. acknowledged: false by default, never auto-flipped. Take-a-hint's ladder-reset semantics fire only on explicit ack — conservative by intent. Future task: explicit ack tool OR derived-from-subsequent-refresh-receipt timing.

  • Outcome tracking ("did this recommendation actually help?") — needs a learning layer beyond 1.5 scope.

The honest split is intentional: hosted = fast, auditable, WASM-pure; local = full capability including filesystem. We will expand the hosted surface only where it can be done safely and without compromising the model.

Subordinate-not-daemon throughout. The orchestrator NEVER auto-fires the recommended tool. Agents surface the recommendation; the user (or higher agent) decides whether to act. Even severity: 'block' is advisory.

See the public verifier and curl https://mcpaas.live/grok/mcp/v1/info for the current contract.


Ecosystem

One format, every AI platform.

Package

Platform

Registry

grok-faf-mcp (this)

xAI Grok

npm + Homebrew

claude-faf-mcp

Anthropic

npm + MCP #2759

gemini-faf-mcp

Google

PyPI

rust-faf-mcp

Rust

crates.io

faf-mcp

Cursor, IDE's, VS Code

npm

faf-cli

Terminal CLI

bunx, npm + Homebrew

Same project.faf. Same scoring. Same result. Different execution layer.


Voice variant — grok-faf-voice (VML)

.fafm 🐘🎙️ — the voice variant of the .faf 🐘 family.

grok-faf-voice is the reference implementation of the Voice Memory Layer (VML) — what your voice agent remembers across sessions, devices, and model switches. Companion to grok-faf-mcp:

  • grok-faf-mcp (this) — .faf Foundational Context Layer for Grok via MCP-on-a-URL.

  • grok-faf-voice — .fafm Voice Memory Layer (VML) for Grok Voice via LiveKit + xAI realtime.

Same family. Different surface. Voice swappable; memory permanent.

PyPI · GitHub · Onboarding


For the xAI / Grok Build team

This MCP server was built for Grok and continues to be shaped by direct feedback from Grok itself.

Open for deeper collaboration on:

  • Native Grok Build integration

  • .fafm memory layer

  • refresh_faf primitives inside the Grok TUI / CLI

  • Any other context or orchestration features the team needs

I ship fast, test rigorously, and dogfood everything live on https://grok.faf.one and https://mcpaas.live/grok/mcp/v1.

Happy to:

  • Open PRs

  • Run private dogfood sessions

  • Jump on a call

  • Or just ship whatever moves the needle

Real software only. Just say the word.


Contributing

PR conventions, code style, CI doctrine, MCP-tool contribution path, npm publish discipline, architecture decisions: CONTRIBUTING.md.

xAI / Grok devs welcome — TL;DR setup at the top.


For xAI / Grok Build team

Open for deeper native integration, .fafm memory layer, or Grok Build CLI collaboration.
Happy to ship PRs, dogfood, or jump on a call. Just say the word.


Citation

If you use grok-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

BibTeX

@article{wolfe2025faf,
  title     = {Format-Driven AI Context Architecture: The .faf Standard for Persistent Project Understanding},
  author    = {Wolfe, James},
  year      = {2025},
  month     = {nov},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.18251362},
  url       = {https://doi.org/10.5281/zenodo.18251362}
}

@article{wolfe2026fafa,
  title     = {Why Agents Need a Passport: .fafa — Portable Identity for the Agentic Era},
  author    = {Wolfe, James},
  year      = {2026},
  month     = {aug},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.21951641},
  url       = {https://doi.org/10.5281/zenodo.21951641}
}

License

MIT — Free and open source



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 · Talk to my Agent →

Available Tools

12 tools
faf_get_orchestration_policyA
Read-only
Inspect

Introspect the effective orchestration policy WITHOUT running the orchestrator. Returns { tier, thresholds, source, overrides_applied } — what aggressiveness tier the next faf_orchestrate_recommendation call would use, and whether it came from defaults or a .faf:orchestration: override. No drift detection, no signals, no receipt — pure introspection. Read-only.

Inputs: path.

faf_initA
Destructive
Inspect

Create a project.faf — the IANA-registered context file (application/vnd.faf+yaml) that gives Grok persistent project DNA (stack, structure, intent) in one portable file, so an agent reads the whole project cold each session instead of re-discovering it. Writes project.faf to the target directory (created if missing) and refuses to overwrite an existing context file unless force is set. Run once at project start, or with force to regenerate, then call faf_score to check readiness. Returns a text confirmation with the written file path and the detected project type.

Inputs: path, force, directory, projectName.

faf_orchestrate_recommendationAInspect

Heavy orchestrator — given current substrate state, returns a structured recommendation about drift: which refresh to call (or no_action), why, how severe, and the underlying signals. ADVISORY ONLY — never auto-fires. Composes the full 1.5 substrate (drift detection · contradiction check · repeat-offender · take-a-hint · refresh history). Writes a recommendation receipt on every call (auditable trail, no silent decisions). Read-only WRT substrate state.

Inputs: no named parameters.

faf_scoreA
Read-only
Inspect

Score a project's AI-readiness 0–100% against the fixed 33-slot context model — how much project DNA an agent has before it has to guess. Deterministic: the same .faf always scores the same. Returns the score, tier, and per-slot breakdown.

Inputs: details.

faf_syncA
Idempotent
Inspect

Sync project.faf into your AI context files: the ones that exist (CLAUDE.md, .cursorrules, .windsurfrules, .clinerules), or CLAUDE.md when there are none (target: all writes every one). Non-destructive: puts a structured .faf block at the top and keeps your own text; a file faf cannot safely write (not UTF-8, a link out of the project) is left unchanged and named in the reply. One way: never writes project.faf.

Inputs: target.

faf_trustA
Read-only
Inspect

Validate a project.faf with faf-cli — confirm the context file is well-formed and parses cleanly before an agent grounds on it, and report its always-33 score. The pre-flight trust check: never build on a broken context layer.

Inputs: path.

rag_cache_clearA
Idempotent
Inspect

Clear the LAZY-RAG cache — drop all cached retrievals so the next rag_query rebuilds from source. Use when the underlying context has changed and you want fresh results instead of cached ones.

Inputs: no named parameters.

rag_cache_statsA
Read-only
Inspect

Get LAZY-RAG cache statistics - hits, misses, hit rate, cache size

Inputs: no named parameters.

rag_queryB
Read-only
Inspect

Ask a question with RAG-enhanced context from xAI Collections. Uses LAZY-RAG cache for 100,000x speedup on repeated queries.

Inputs: question (required).

refresh_blendAInspect

Baked-in two-intensity refresh (Cmd+R / Cmd+Shift+R analog). Fires BOTH refresh_faf + refresh_fafm in one call. mode: "blend" (default) = light .faf + delta .fafm — the everyday re-ground. mode: "nuke" = light .faf + verbatim .fafm — the hard reload for polluted session memory. Intensity matches drift rate per layer.

Inputs: mode, path, soul, since, +1 more.

refresh_fafBInspect

Re-ground on the live .faf — re-read + re-score the current project DNA, report drift vs your last-known score, and return the fresh context. The explicit re-grounding primitive for long sessions: drift → refresh → re-grounded. Built for Grok, by request.

Inputs: path, baseline.

refresh_fafmAInspect

Reload the latest structured memory (.fafm) for one or more souls into the current session. Returns a stamped delta by default (added/updated facts since last refresh or a given timestamp). Use verbatim: true to receive the full current .fafm content instead. Reads your files and writes a refresh receipt (.fafm-refresh-receipts.json) to the project folder. Always returns a content hash + timestamp stamp. Complements recall, load_soul, and etch — does not replace them.

Inputs: soul, since, verbatim.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    B3.4/5.0

    Scored across 12 tools

    Disambiguation2/5

    The refresh/score cluster has genuinely unclear boundaries: refresh_faf, refresh_fafm, and refresh_blend overlap, with refresh_blend literally composing the other two, and faf_orchestrate_recommendation exists mainly to advise which of them to call. Similarly, faf_score and faf_trust both report scores (0-100% vs always-33), and refresh_faf also re-scores, so an agent has several plausible entry points for the same outcome. The RAG tools are cleanly separated, but the core FAF family is muddy.

    Naming Consistency2/5

    Two competing paradigms coexist: noun-prefixed actions (faf_sync, faf_trust, faf_score, faf_init) and verb-first actions (refresh_faf, refresh_fafm, refresh_blend), plus an outlier like faf_get_orchestration_policy that breaks the faf_<verb> pattern. The rag_ namespace is consistent, but the overall set mixes verb placement and prefixes, making it hard to predict a tool name from its action.

    Tool Count4/5

    Twelve tools sit comfortably in the well-scoped 3-15 range for a context-management server, and the RAG trio plus the core FAF lifecycle each justify their existence. However, the refresh family (refresh_faf, refresh_fafm, refresh_blend, plus an orchestrator) could arguably be consolidated, so it is slightly over-provisioned rather than perfectly tight.

    Completeness3/5

    The surface covers create (faf_init), read/score (faf_score, refresh_faf), validate (faf_trust), sync (faf_sync), and RAG, but there is no direct edit/update or delete operation for project.faf. The .fafm memory lifecycle is also partial here — refresh_fafm explicitly defers to external recall/load_soul/etch tools that are not in this server, leaving gaps an agent must work around.

    Maintenance

    ActivityActive
    ResponsivenessMostly unanswered

    Related MCP Connectors

    Related MCP Servers

    • A
      license
      A
      quality
      B
      maintenance
      Persistent project context for Google Gemini. 12 MCP tools for .faf Project DNA — auto-detect your stack, validate, score, and sync across CLAUDE.md, GEMINI.md, and AGENTS.md. Python/FastMCP. IANA-registered format (application/vnd.faf+yaml). 183 tests. One file, every AI platform.
      13
      489 PyPI
      2
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Persistent project context in Rust. 8 MCP tools via rmcp SDK — parse, validate, score, compress, discover, and token analysis. Single binary, zero config. IANA-registered format (application/vnd.faf+yaml). One file, every AI platform.
      407 npm
      4
      MIT