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SmartTokenGuard

Checks AI video renders, reviews keyframes, and returns spend-aware PASS, RETRY, SWITCH, or HALT decisions.

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Install SmartTokenGuard

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "smarttokenguard": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/nwjnjjj/smart-token-guard",
        "smart-token-guard"
      ],
      "env": {
        "STG_LICENSE_KEY": "YOUR_VALUE_HERE"
      }
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Values are written into the snippet above so you can paste it as is. They never leave your browser.

Automated check passed— started and listed 6 tools correctly (1d ago).

Overview

SmartTokenGuard MCP server inspects AI-generated images and video for technical defects before another paid render step. It measures issues such as blur, exposure, black frames, frozen segments, flicker, cuts, impacts, flashes, and drift against a reference image. Its QA gate combines measurements with an assistant's review of returned frames to recommend PASS, retry, fallback, or halt decisions. Use it when rendering through ComfyUI, Kling, Runway, Veo, Hailuo, Seedance, or other video tools and you want local spend tracking.

Use cases

  • Inspect keyframes before generating paid video clips
  • Gate renders with retry, fallback, or halt decisions
  • Track render spending and pass rates by project
  • Detect impacts, flashes, and cuts for sound placement

Key features

  • Image and video quality inspection
  • Reference-based color and lighting checks
  • PASS, retry, fallback, and halt decisions
  • Per-project local spend ledger
  • Event-frame detection
  • Remote MCP and A2A access

Tools6

What SmartTokenGuard lets your agent do. Expand a tool for its parameters.

Verified live~876 tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 1d ago

Recent health check succeeded.

9/9 checks healthy over the last 44d

What it can do

From the 6 tools this server returned when we ran it.

Read-only tools · 6
Network at startup2 connections
Files written at startup16

Observed 2026-11-11 in an isolated sandbox. A description of reach, not a security audit.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 14d ago via OSV.dev · smart-token-guard (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

Last commit1mo agoMost recent push to the default branch.
Availability100%Our rolling endpoint + install checks that succeeded.
Install checkPassedOur sandbox started it and listed its tools.
Tools exposed6Callable tools this server registers over MCP.

How SmartTokenGuard works

What SmartTokenGuard MCP server does

SmartTokenGuard MCP server evaluates still images and video clips before an AI video workflow proceeds to another paid stage. It can inspect keyframes and rendered media for technical problems including soft focus, blur, overexposure, underexposure, black frames, frozen sections, flicker, hard cuts, impacts, flashes, and color or lighting changes relative to a reference image. The inspection response includes review frames so an assistant can examine hands, faces, and identity.

The service is intended for workflows using tools such as ComfyUI, Kling, Runway, Veo, Hailuo, and Seedance, but the material does not require a specific video-generation provider. A rejected still can be identified before paying for a video clip.

How it works

Measured inspection results feed the QA gate along with defects observed by the assistant. The gate returns one of five outcomes: PASS, RETRY_SAME_MODEL, SWITCH_TO_FALLBACK, HALT, or HALT_BUDGET. It can also provide corrective actions, such as negative-prompt terms, reduced motion, or a different seed. The decision can include a render model, cost, and fallback model.

The local client keeps a per-project spending ledger on the user's computer. Spend reports expose total project cost, attempts per successful shot, first-attempt pass rate, and keyframes rejected before video generation. Event detection identifies exact frames for impacts, flashes, and cuts, which can help place sound effects.

Setup and configuration

The local MCP server requires uv. Claude Desktop and Cursor can launch it with uvx from the Git repository, while Claude Code can register the same command with claude mcp add. VS Code uses an .vscode/mcp.json stdio entry. Each local configuration supplies STG_LICENSE_KEY; the license key from the Polar receipt is the API key.

A Python client is also available from the Git repository. Its Client reads the same environment variable and exposes inspection and QA-gate operations. The HTTP API provides /v1/inspect, /v1/qa-gate, /v1/account, and /v1/capabilities.

Remote use is available through Streamable HTTP at the documented MCP endpoint, using a Bearer license key. Remote inspection accepts public HTTPS media URLs, downloads the files for analysis, and removes them afterward. A2A access is also documented through the agent card and JSON-RPC endpoint.

Tools and capabilities

The SmartTokenGuard MCP server exposes these local tools:

  • stg_inspect_render measures image or video defects and returns review frames.
  • stg_qa_gate produces a render decision and corrective actions while updating the spend ledger.
  • stg_detect_events locates impacts, flashes, and cuts.
  • stg_spend_report summarizes project spending and pass statistics.
  • stg_account and stg_pricing provide license and plan information.

The remote MCP interface lists URL-based inspection, QA gating, account, and pricing tools. Plans provide 300 or 3,000 checks per month, depending on the selected tier.

Limitations and notes

Each file is limited to 32 MB, 60 seconds, and 1080p, with JPEG, PNG, WebP, MP4, MOV, WebM, and MKV supported. The service allows 30 requests per minute. Remote URLs must be publicly reachable HTTPS addresses; private and internal addresses are refused.

Uploads go to the hosted API, are analyzed in a temporary file, and are deleted before the response is sent. The documentation states that files are not stored or used for training. Project ledgers remain under ~/.smart-token-guard/ locally. Technical checks do not certify anatomy, identity, copyright, or story quality. The repository client is MIT licensed, while the hosted API requires a paid license.

Read the full README View source on GitHub

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Frequently Asked Questions about SmartTokenGuard

SmartTokenGuard is an MCP server that connects AI agents to rendered images and video from tools such as ComfyUI, Kling, Runway, Veo, Hailuo, and Seedance. Its main tools inspect media, detect events, apply QA gates, report spending, and provide account or pricing details.

Install it with uv and launch the Git repository through `uvx`, supplying `STG_LICENSE_KEY` in the MCP client configuration.

SmartTokenGuard accepts JPEG, PNG, WebP, MP4, MOV, WebM, and MKV files. Each file can be up to 32 MB, 60 seconds, and 1080p, with a limit of 30 requests per minute.

No, uploaded files are analyzed in a temporary file and deleted before the response is sent. The documentation says media is not stored or used for training; local project ledgers remain under `~/.smart-token-guard/`.

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