Shumei Anti-Fraud MCP for AI. Scan media, text, and devices for risk instantly.

Q: How do I use checktextrisk?

checktextrisk takes any block of text—like a comment or chat message—and returns an immediate verdict on whether it's spam, abusive, or otherwise restricted. This is perfect for real-time moderation.

Q: Is checkdevicerisk only for IP addresses?

No. checkdevicerisk handles both specific IP addresses and unique Device IDs. You can use it to check if a user is operating through an emulator or using a VPN, regardless of the input type.

Q: What kind of content does checkimagerisk scan?

checkimagerisk scans for NSFW material and policy-violating graphical elements. You give it an image URL, and you get a pass/fail verdict on its appropriateness.

Q: Can I check audio files with Shumei Anti-Fraud MCP Server?

Yes, use checkaudiorisk. This tool lets your agent predict risks associated with an audio clip, helping you moderate voice chat rooms for things like hate speech.

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

Connect to your AI in seconds.

Shumei Anti-Fraud provides enterprise-grade risk assessment built for AI agents. It lets your client check text, images, audio clips, and device IDs against known fraud patterns instantly.

Use it to automatically filter spam, detect NSFW content, or block traffic from suspicious VPNs before they hit your platform.

What your AI can do

Check audio risk

Predicts risk associated with an uploaded audio clip, identifying potential violations like hate speech or restricted content.

Check device risk

Determines if a provided device ID or IP is fraudulent or indicates the use of a VPN or emulator.

Check image risk

Scans an image for NSFW content, restricted material, or policy violations.

+ 1 more capabilities included

Check text for abuse or spam

Sends raw text strings to Shumei and receives a classification indicating if the content is abusive, spammy, or contains restricted material.

Determine device fraud risk

Takes an IP address or unique Device ID and checks it against databases used to identify VPNs, emulators, or other fraudulent sources.

Scan images for restricted content

Accepts image URLs or data and scans them immediately for NSFW material or policy-violating graphical elements.

Predict risk from audio clips

Analyzes uploaded audio files to predict associated risks, such as hate speech or copyrighted material detection.

Ask an AI about this

Included with Plan

Waiting for input…

AI Agent

Shumei Anti-Fraud MCP Server: 4 Tools

Use these four specialized tools to perform real-time risk checks on text, images, audio clips, and device identifiers within your AI agent's workflow.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using Shumei Anti-Fraud on Vinkius

Check Audio Risk

Predicts risk associated with an uploaded audio clip, identifying potential violations like hate speech or restricted content.

Check Device Risk

Determines if a provided device ID or IP is fraudulent or indicates the use of a VPN...

Check Image Risk

Scans an image for NSFW content, restricted material, or policy violations.

Check Text Risk

Scans a piece of text to detect spam, abuse, or explicit content.

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

Start a conversation

Open a new chat. The Shumei Anti-Fraud integration is available immediately — no restart needed.

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "shumei-anti-fraud": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the Shumei Anti-Fraud tools with full Vinkius guardrails applied.

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"shumei-anti-fraud": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

Import from OpenAPI, Swagger, or YAML specs
Create Agent Skills with progressive disclosure
Deploy to edge with MCPFusion framework
Built in DLP, auth, and compliance on every call
Real time usage dashboard and cost metering
Publish to catalog or keep private

Start building

Make Your AI Do More

Start with Shumei Anti-Fraud, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

Use this MCP plus 5,100+ others, all in one place
Add new capabilities to your AI anytime you want
Every connection is secured and compliant automatically
Track usage and costs across all your servers
Works with Claude, ChatGPT, Cursor, and more
New servers added to the catalog every week

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Shumei Anti-Fraud. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

VINKIUS INFRASTRUCTURE

Cloud Hosted

Managed infra

V8 Isolated

Sandboxed per request

Zero-Trust Proxy

No stored credentials

DLP Enforced

Policy on every call

GDPR Compliant

EU data residency

Token Compression

~60% cost reduction

Your data is protected. See how we built it.

Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 4 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Moderating UGC without a full backend team is hell.

Right now, handling user content means juggling three things: filtering text for abuse, checking if uploaded images are okay, and verifying the user's IP address. You end up writing messy code that calls multiple different third-party services, each with its own rate limits and error codes. It’s a total nightmare to keep working.

With this MCP server, your agent handles all of it. You write one function call—for example, checking an upload. The agent runs `check_image_risk` for the media, then passes the caption through `check_text_risk`, and finally validates the source with `check_device_risk`. All in one clean workflow.

Shumei Anti-Fraud MCP Server: Streamline risk checks across all media.

You eliminate separate modules for text, image, and device validation. You don't need to worry about which service handles what; the agent manages that orchestration automatically. It takes complex policy enforcement and turns it into a simple function call.

The difference is control. Everything runs through one reliable API endpoint, giving you consistent results and keeping your codebase clean of messy integration logic.

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What your AI can actually do with this

This server is built for your AI agent, giving it enterprise-grade risk assessment capabilities right out of the box. It lets you check text, images, audio clips, and device IDs against known fraud patterns instantly, letting you automatically filter spam or block traffic from suspicious sources before they ever hit your platform.

Text Abuse Detection with check_text_risk: You feed this tool a raw string of text, and it immediately returns a classification. This tells you if the content is abusive, how likely it is to be spam, or whether it contains restricted material. If the score crosses a certain threshold, you know exactly what action your agent needs to take—rejecting the input, flagging it for review, or just passing it through.

Image Screening with check_image_risk: You pass in an image URL or direct data stream, and the server scans it right away. It checks for NSFW material or any graphical elements that violate your platform's policy. This tool doesn't just say 'yes' or 'no'; it gives you a risk assessment of the visual content.

Audio Risk Prediction via check_audio_risk: When an uploaded audio file comes in, this function analyzes it to predict associated risks. It detects things like hate speech or copyrighted material usage, helping you filter out problematic media before your system processes it further. You get a risk score based on the content of the sound clip.

Device Fraud Validation with check_device_risk: If you receive an IP address or a unique Device ID, this tool runs checks against massive databases. It determines if that source is fraudulent, if it's using a VPN to mask its location, or if it originated from an emulator. This lets your agent block traffic from known bad actors and spoofing sources.

Your AI client doesn't need complex backend services to handle moderation; you just call the right tool. You can build logic that dictates action based on these immediate risk verdicts. For example, a user submits text (check_text_risk), then uploads an image (check_image_risk), and their device connects from a suspicious IP (check_device_risk).

Your agent runs all three checks in sequence. If any single check fails—say, the text is abusive or the device ID is flagged as a VPN—you can block the whole transaction immediately.

The server lets you perform live audits on user activity data. When your agent processes chat logs or large batches of submitted content, it doesn't just look at individual pieces; it gets an overall view of suspicious behavior across entire streams. This means you catch patterns of abuse, not just isolated incidents.

When setting up your workflow, you simply connect your AI client to this server and access the tools by name in your agent’s logic. You call check_text_risk for text inputs; you call check_image_risk for visuals; you call check_audio_risk for sound files; and you call check_device_risk whenever an IP or device identifier comes through.

Each tool provides a definitive, actionable risk score that dictates the next step: allow it, warn on it, or block it entirely.

Built · Hosted · Managed by Vinkius Shumei Anti-Fraud MCP Server - Detect Risk Across Media

Server ID 019d8480-4d12-7158-a291-8a98af9bb9bb

Vinkius Inspector

Compliance Grade A+

Score 100/100

Report View Report ↗

Here's how it actually works

The bottom line is: your AI agent gets an instant, actionable 'pass' or 'fail' grade on any content or device before it ever reaches your core application logic.

First, the user prompts your AI agent with a piece of content (e.g., 'Check this photo' or 'Analyze this chat log').

The agent decides which tool to run—like check_image_risk—and passes the specific data payload (the image URL) to the Shumei server.

Shumei processes the request and returns a structured JSON response containing the risk verdict, score, and classification details.

Who is this actually for?

This server is for the Ops Engineer who needs to build automated safety wrappers into production code. It’s also crucial for Trust & Safety teams managing high-volume, multi-media content feeds, and Community Managers dealing with chat transcripts that need instant compliance checks.

Trust & Safety Agent

Uses check_device_risk to intercept new account creations from suspicious IPs or known VPNs. They also run check_text_risk on chat logs for policy violations.

Backend Developer

Integrates the server into user upload endpoints. For instance, running check_image_risk on avatars or check_audio_risk on voice messages before saving them to the database.

Community Manager

Automates moderation by passing entire chat transcripts through check_text_risk, allowing the agent to flag and quarantine abusive user conversations instantly.

What Changes When You Connect

Stops spam before it lands. By using check_text_risk, your agent can analyze entire user inputs—not just keywords—to catch sophisticated advertising or abusive language in chat logs.

Blocks bad actors at the source. Running check_device_risk validates IPs and device IDs, letting you reject connections from known VPNs or emulators attempting to bypass limits.

Maintains platform safety for all media types. Whether it's an image (check_image_risk), audio clip (check_audio_risk), or text block, the server provides a unified risk score and verdict.

Reduces manual review load. Instead of having human moderators manually check every suspicious item, your agent runs check_text_risk and automatically flags items for quick human attention only.

Protects against data poisoning. When developers use this to validate user avatars or uploads, they ensure that only clean media gets stored, preventing the platform from being compromised by bad assets.

See it in action

01 01

Stopping spam in a forum thread

A Community Manager finds a suspicious post. Instead of manually reading it, they ask their agent to run check_text_risk on the entire body. The server instantly returns a 'Spam/Advertising' flag and an actionable rejection status.

02 02

Preventing bot account creation

A new user attempts to sign up from a known corporate VPN IP range. Your agent runs check_device_risk on the provided IP address, which returns 'High Risk/VPN Detected.' The system blocks the signup attempt automatically.

03 03

Moderating uploaded profile pictures

A user uploads a photo as their avatar. Before saving it, the backend triggers check_image_risk. If the server returns an 'NSFW' or 'Restricted Content' flag, the upload fails immediately, and the user gets an error.

04 04

Filtering live audio chat rooms

In a voice chat environment, one participant sends a problematic audio clip. The agent intercepts this by calling check_audio_risk. If the server flags it for hate speech, the agent alerts moderators and prevents playback.

The honest tradeoffs

Checking content piece-by-piece

Anti-pattern

Having a separate module that only checks text risks, and another one that only handles image uploads. This leaves gaps in the overall platform safety.

The Fix

Use this single MCP Server to orchestrate all checks. For example, on upload, run check_device_risk first (IP), then pass both the text caption through check_text_risk, and finally, check the image with check_image_risk. This covers everything.

Assuming client-side filtering is enough

Anti-pattern

Relying solely on the user's browser or local code to filter content. These are easily bypassed by determined bad actors.

The Fix

Always validate inputs server-side using this MCP Server. Pass all incoming text through check_text_risk and all media through check_image_risk/check_audio_risk. Never trust the client.

Writing custom risk logic

Anti-pattern

Trying to build a proprietary database of spam keywords or banned IP ranges. This is huge maintenance work and quickly falls out of date.

The Fix

Let Shumei handle the core risk intelligence. Use check_device_risk for IPs and use check_text_risk for language policy violations; it’s built on industry-leading data.

When It Fits, When It Doesn't

Use this server if your primary problem is consistent, multi-layered content moderation across text, media, and user identity. You need a single point of truth to determine 'safe' or 'unsafe.'

Don't use it if you only need simple keyword filtering (a basic regex check will do) or if your fraud risk is purely geographical and doesn't involve device fingerprinting.

If your needs are deep, specialized compliance—like financial transaction monitoring—you might look at dedicated FinTech API wrappers. But for general user-generated content (UGC), this combination of check_text_risk, check_image_risk, and check_device_risk provides the most robust foundation available.

Questions you might have

How do I use `check_text_risk`? +

check_text_risk takes any block of text—like a comment or chat message—and returns an immediate verdict on whether it's spam, abusive, or otherwise restricted. This is perfect for real-time moderation.

Is `check_device_risk` only for IP addresses? +

No. check_device_risk handles both specific IP addresses and unique Device IDs. You can use it to check if a user is operating through an emulator or using a VPN, regardless of the input type.

What kind of content does `check_image_risk` scan? +

check_image_risk scans for NSFW material and policy-violating graphical elements. You give it an image URL, and you get a pass/fail verdict on its appropriateness.

Can I check audio files with Shumei Anti-Fraud MCP Server? +

Yes, use check_audio_risk. This tool lets your agent predict risks associated with an audio clip, helping you moderate voice chat rooms for things like hate speech.

What do I need to set up and use the `check_device_risk` tool? +

You must provide a valid Access Key obtained from the Shumei Control Panel. This key authorizes your agent to communicate with the risk service. Without this credential, none of the tools will execute.

If `check_text_risk` encounters an API error or times out, how should my agent handle it? +

Your agent should implement basic retry logic with exponential backoff. If repeated retries fail, the agent must log the failure and proceed with a default 'Unknown Risk' flag rather than stopping execution.

Are there rate limits when calling `check_image_risk` or other tools? +

Yes, the service imposes rate limits to manage usage. Exceeding the defined quota will result in a 429 HTTP error code. Your client must monitor this code and throttle its requests accordingly.

How is data privacy handled when I run `check_device_risk`? +

Shumei handles all input identifiers, like IP addresses or Device IDs, according to strict privacy protocols. The service uses the data only for immediate risk scoring and does not retain personal records.

Does my image data get stored on their servers? +

Shumei caches media briefly for anti-spam scanning operations but does not persist non-flagged uploads permanently. Consult your enterprise agreement for strict retention periods.

Connect to your AI in seconds.

Check audio risk

Check device risk

Check image risk

Shumei Anti-Fraud MCP Server: 4 Tools

Make your AI actually useful.

Check Audio Risk

Check Device Risk

Check Image Risk

Check Text Risk

Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

Works with Claude, ChatGPT, Cursor, and more

Moderating UGC without a full backend team is hell.

Shumei Anti-Fraud MCP Server: Streamline risk checks across all media.

What your AI can actually do with this

Here's how it actually works

Who is this actually for?

What Changes When You Connect

See it in action

Stopping spam in a forum thread

Preventing bot account creation

Moderating uploaded profile pictures

Filtering live audio chat rooms

The honest tradeoffs

Checking content piece-by-piece

Assuming client-side filtering is enough

Writing custom risk logic

When It Fits, When It Doesn't

Questions you might have