Tar Engine MCP Server

TAR Engine — Audit AI skill safety before you ship

Audit AI skill safety in CI or from your agent. Static rules + semantic LLM analysis + adversarial prompt fuzz with a configurable victim model. BYOK for the semantic and adversarial layers. Free tier hosted on tarai.dev; multi-victim adversarial ensembles are available on the hosted advanced tier.

tar-engine is a security + quality audit pipeline for AI agent skills. It covers SKILL.md (OpenClaw, Claude Code), Codex skill.yaml, Claude Code custom commands (.claude/commands/*.md), and OpenCode configs out of the box. Designed to drop into CI without changing how your skills are authored.

Drop into CI — tar-engine scan ./skills --min-score 70 exits 1 if any skill scores below the threshold. See CLI usage below.

Or wire into your agent — Claude Code, Cursor, or Codex CLI can call the audit pipeline as an MCP tool. See MCP install below.

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CLI — audit AI skill from the command line

The tar-engine CLI walks a directory, audits every skill it finds, and exits with a CI-friendly status code.

# audit every skill under ./skills, fail the build if any scores below 70
tar-engine scan ./skills --min-score 70

# list discovered skills without auditing
tar-engine list ./skills

# JSON output for downstream processing
tar-engine scan ./skills --json

Discovery covers five formats out of the box:

Each audit payload bundles the primary skill file plus sibling .sh / .py / .js / .ts / .yaml / .json helper files in the same directory (200 KB cap). Catches the "SKILL.md clean but install.sh malicious" pattern.

GitHub Actions example:

- name: Audit AI skills
  run: |
    uvx --from "git+https://github.com/qingxuantang/tar-engine@v0.3.0" \
      tar-engine scan ./skills --min-score 70

Pre-commit hook:

#!/usr/bin/env bash
tar-engine scan ./skills --min-score 80 || exit 1

Exit codes: 0 clean, 1 below threshold, 2 usage/missing path.

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Install

TAR Engine ships an MCP server as a Python package, runnable with uvx — no Docker. By default it talks to the hosted backend at tarai.dev (free, rate-limited).

Read this first — what you're trusting

• Where SKILL.md goes. With the default config the MCP server POSTs the SKILL.md content you ask it to audit to https://tarai.dev. We don't write skill text to disk or log it, but it does leave your machine. If you're auditing proprietary or sensitive skills, self-host and set TAR_ENGINE_URL=http://localhost:8765.
• No silent key forwarding. The server does NOT forward your OPENAI_API_KEY. Semantic + adversarial audit layers require an explicit opt-in via TAR_ENGINE_BYOK_OPENAI_KEY in the MCP server config — see BYOK below.
• What environments work. Claude Code CLI, Cursor, Codex CLI — anywhere your agent can launch a subprocess. Claude Desktop, Claude.ai web, and the mobile apps cannot install local MCP servers; hosted endpoint for those is coming — waitlist on tarai.dev.

Step 0 — install uv (one-time, ~5 seconds)

The package is run via uvx, which comes with uv. Install once:

# macOS / Linux
curl -fsSL https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
irm https://astral.sh/uv/install.ps1 | iex

# Alternative — via pipx if you don't trust curl|sh
pipx install uv

# Alternative — via pip
pip install --user uv

Verify with uvx --version.

Step 1 — register with your agent

One-click: grab setup-mcp.sh and run it — it checks for uv, prompts for an optional BYOK key (hidden input, never written to disk by the script), and registers the server with your agent:

curl -fsSL https://raw.githubusercontent.com/qingxuantang/tar-engine/master/setup-mcp.sh -o setup-mcp.sh
chmod +x setup-mcp.sh
./setup-mcp.sh                       # Claude Code (default); add --client cursor|codex

Or configure it manually below. Two install paths are supported — pick one:

• From PyPI (recommended for MCP-marketplace users and Anthropic MCPB clients): installs the last released wheel of tar-engine from PyPI. Fastest cold-start, no git required. Command form: uvx --from tar-engine tar-engine-mcp. Pin an exact release with tar-engine==0.3.1 if you want reproducible upgrades.
• From git tag (for tracking a specific commit or unreleased HEAD): the examples below pin to the v0.3.0 release tag. Swap @v0.3.0 for @master if you'd rather track the latest unreleased work, or for a different tag once we cut one.

claude mcp add tar-engine -- uvx --from "git+https://github.com/qingxuantang/tar-engine@v0.3.0" tar-engine-mcp

Verify: /mcp list should show tar-engine Connected. Restart Claude Code so this session picks up the new tool surface, then ask:

Audit this SKILL.md: [paste a skill]

Edit ~/.cursor/mcp.json (or project-level .cursor/mcp.json):

{
  "mcpServers": {
    "tar-engine": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/qingxuantang/tar-engine@v0.3.0", "tar-engine-mcp"]
    }
  }
}

Reload MCP servers in Cursor (or restart the app), then call audit_skill_text from inside Cursor.

Add to ~/.codex/config.toml:

[mcp_servers.tar-engine]
command = "uvx"
args = ["--from", "git+https://github.com/qingxuantang/tar-engine@v0.3.0", "tar-engine-mcp"]

Restart the Codex CLI, then call audit_skill_text.

Most agents accept an MCP server spec with command + args (JSON or TOML). Two supported forms:

PyPI form (recommended, and the canonical form for MCP-marketplace / Anthropic MCPB submissions):

• command: uvx
• args: ["--from", "tar-engine", "tar-engine-mcp"] — pin an exact version with tar-engine==0.3.1 if you want reproducibility

Git-tag form (for tracking a specific commit or unreleased HEAD):

• command: uvx
• args: ["--from", "git+https://github.com/qingxuantang/tar-engine@v0.3.0", "tar-engine-mcp"]

env (optional, applies to both forms):

• TAR_ENGINE_URL=http://localhost:8765 to self-host
• TAR_ENGINE_BYOK_OPENAI_KEY=sk-... to enable semantic + adversarial layers

Reload the agent and call audit_skill_text to verify.

BYOK (semantic + adversarial layers)

By default only the static rule layer runs against your skill — free, deterministic, no LLM cost. To enable the semantic LLM review and the adversarial prompt-fuzz pass, supply your own LLM key explicitly:

"tar-engine": {
  "command": "uvx",
  "args": ["--from", "git+https://github.com/qingxuantang/tar-engine@v0.3.0", "tar-engine-mcp"],
  "env": {
    "TAR_ENGINE_BYOK_OPENAI_KEY": "sk-..."
  }
}

We deliberately do not read OPENAI_API_KEY from your general environment. Most Claude Code / Cursor / OpenAI SDK users have that key set for unrelated purposes — silent relay would be wrong. Set TAR_ENGINE_BYOK_OPENAI_KEY only when you want this MCP server to use your key.

Self-host

If the privacy / latency tradeoff of the hosted backend doesn't work for you, run the engine locally:

git clone https://github.com/qingxuantang/tar-engine
cd tar-engine
cp .env.example .env  # add OPENAI_API_KEY if you want semantic + adversarial
docker compose up -d

Then point the MCP server at it:

"env": {
  "TAR_ENGINE_URL": "http://localhost:8765"
}

Same tool surface, no data leaves your machine, your own OpenAI key, no rate limit beyond what your hardware supports.

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What it is

TAR Engine is an OSS wish machine for AI agents.

You install it on your laptop or server, point it at your LLM key (BYOK), and talk to it in plain English: "Find the best parameters for strategy A." / "Summarize this URL and post a draft to LinkedIn."

The engine:

1. Plans — decomposes the wish into a skill chain
2. Executes — runs the skills (engine-side, no IDE plugin needed)
3. Traces — records every step (tokens, timing, args, outputs)
4. Audits — scans each skill action against a configurable rule set
5. Reflects — opt-in retrospective LLM pass that distills lessons into your profile

This is the OSS core. Curated domain packs — turnkey skill chains for specific verticals like quant trading or content publishing — are sold separately and ship with their own planner templates, domain knowledge, and skill bundles.

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5-minute quickstart

# 1. Clone
git clone https://github.com/qingxuantang/tar-engine.git
cd tar-engine

# 2. Configure
cp .env.example .env
# Edit .env and set OPENAI_API_KEY

# 3. Run
docker compose up -d

# 4. Verify
curl http://localhost:8765/healthz

# 5. Submit your first wish (uses Hello World Pack)
curl -X POST http://localhost:8765/api/cockpit/wish \
  -H "Content-Type: application/json" \
  -H "X-LLM-Api-Key: $OPENAI_API_KEY" \
  -d '{"wish": "echo hello world", "pack": "hello-world"}'

# Response:
# { "task_id": "tsk_...", "status": "queued" }

# 6. Fetch the result
curl http://localhost:8765/api/cockpit/wish/tsk_...

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Why TAR Engine

Three concrete design choices set TAR Engine apart from generic skill marketplaces:

• Engine-side execution. Skills run inside the engine container, not in your IDE. Your IDE doesn't need a plugin. CI / cron / Telegram can all trigger wishes.

• Built-in audit pipeline. L1 static rules + L2 capability bitmap + L3 dynamic LLM review. Every skill action gets scored. Your CFO / compliance officer can trust the trace.

• Retrospective + profile. Wishes don't just run; they teach the engine. The retrospective extracts lessons from each run, stores them on your profile, and injects them into the next plan. The engine gets sharper the more you use it.

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What's in this repo

tar-engine/
├── backend/
│   ├── app.py                    # FastAPI entry
│   ├── cockpit/                  # Wish machine: planner / dispatcher / skill executor / trace / retrospective
│   ├── auditor/                  # Audit pipeline: L1 / L2 / L3 + risk scorer + orchestrator
│   ├── adapters/                 # IDE/runtime adapters (Claude Code, Codex CLI, generic webhook)
│   └── knowledge/                # Knowledge L3 RAG (LlamaIndex + ChromaDB)
├── packs/
│   ├── hello-world/              # Reference demo pack — 5-min install verifier
│   └── postall-content/          # Multi-platform content publishing pack (requires postall-agent)
├── frontend/                     # Web UI (static, optional)
├── docker-compose.yml
├── Dockerfile
├── requirements.txt
└── docs/                         # Architecture, deployment, contribution notes

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Curated domain packs (paid, not in this repo)

The OSS engine is enough to author your own packs. If you want a turnkey domain solution, we ship curated packs as paid add-ons:

• Quant Trading Pack — strategy research, factor mining, backtest audit, live signal monitoring (ships first)
• Content Publishing Pack — multi-platform content generation with built-in QA gate and cross-platform consistency audit (ships second)

Packs come with curated skill chains, domain-tuned planner few-shot templates, domain knowledge corpora, profile schemas, and the audit dashboard pre-wired.

Visit tarai.dev for pack details and pricing.

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Audit reports (米其林指南)

We publish weekly audit reports of popular open-source skills from major skill platforms — Smithery, Claude Hub, MCPHub. Each report includes a security score, specific findings, and remediation suggestions. The audit pipeline used to generate these is the same one shipped in this OSS engine. Try it on your own skills:

curl -X POST http://localhost:8765/api/cockpit/audit/static \
  -H "Content-Type: application/json" \
  -d '{"skill_text": "<your SKILL.md content>"}'

Read the latest reports on AI 米其林指南 — the public guide of skill audits, with a live Playground you can paste any SKILL.md into.

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Status

This is the 0.1.0 minimum public release. Core wish machine + audit pipeline

• trace + retrospective + hello-world pack all work. The Web UI is functional but spartan. Contributor docs and CI are not yet polished.

What works today:

• ✅ Wish → planner → skill chain → execution
• ✅ Execution trace with token + timing instrumentation
• ✅ Audit pipeline (L1 static rules + L2 capability bitmap)
• ✅ Hello World Pack runs end-to-end via curl
• ✅ Docker Compose deployment

What's coming (next 4-12 weeks):

• AI retrospective with lesson distillation + profile injection
• Web UI polish + run timeline panel
• Telegram bot entry
• L3 dynamic LLM audit
• Audit content engine for skill ecosystem coverage (米其林指南 model)
• Quant Pack (first paid pack — domain-curated quant trading workflow)
• Premium Content Pack enhancements on top of the OSS PostAll pack

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Contributing

This is early days. The fastest way to help is:

1. Try the 5-minute quickstart and report friction
2. Author your own pack and link it in Discussions
3. Run the audit pipeline on your favorite skill and share findings

PR contributions to backend/cockpit/, backend/auditor/, and packs/hello-world/ are welcome. The paid packs (quant trading, content publishing) are first-party only — we won't accept PRs that add UGC packs to the packs/ directory.

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License

Apache 2.0 — see LICENSE for the full terms.

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Acknowledgments

TAR Engine started as an audit tool for AI quant trading workflows. It grew into a general-purpose wish machine through real conversations with quant engineers, content creators, and security teams who all asked variations of the same question: "How do I make my AI agents do real work without losing my job to a hallucinated trade?"

Built by Mark Zhou with Claude Code.