Sift (Chargeback) MCP. Automate fraud scoring and dispute resolution.
Sift (Chargeback) manages your entire fraud defense lifecycle from a single conversation. Connect this MCP to instantly check user risk scores, report suspicious chargebacks, and apply manual decisions—all without opening the Sift dashboard. It lets you run real-time dispute resolution and audit user history directly through any AI agent.
Give Claude and any AI agent real-world access
The system fetches the latest fraud score assigned to any user.
It retrieves the specific flags or labels Sift has applied to a user's account history (e.g., $bad).
You can instruct your agent to manually accept, block, or change the status of a user's account.
The MCP sends formal chargeback reports to Sift, updating the risk profile for the involved party.
It logs custom activity like a user logging in or completing a transaction to refine Sift's machine learning model.
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What AI agents can do with Sift (Chargeback) MCP: 8 Available Tools
Use these tools to manage every aspect of fraud prevention, from checking user scores to reporting complex chargeback events.
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 Sift (Chargeback) MCPApply User Decision
Manually applies a decision, such as blocking or approving, to a specific user account.
List User Decision History
Retrieves a chronological list of all decisions and actions ever applied to a given...
Get User Fraud Labels
Fetches the specific fraud labels (like $bad or $good) Sift has assigned to a user's...
Get User Fraud Score
Checks and returns the real-time numerical fraud risk score for any specified user.
List Sift Decisions
Shows all the available decision types or actions that Sift recognizes.
List Sift Workflows
Lists and provides visibility into your currently configured fraud prevention workflows within Sift.
Report Sift Chargeback
Submits a formal chargeback event report to Sift, triggering an update of the user's risk profile.
Track Sift Event
Logs general events (like logins or transactions) into Sift so they can train their...
Security and governance baked right in.
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Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Handling fraud disputes shouldn't feel like logging into three different dashboards at 2 AM.
Right now, when a customer claims fraud or an order is flagged, the process is manual misery. You open Sift to check the user score; you switch to your ticketing system to read the dispute details; then you open a spreadsheet to find the history of decisions—and finally, you copy all that information into an email for review. It's tedious, slow, and prone to human error.
With this MCP connection, you simply ask your agent to check the risk score or list the decision history for the account in one chat thread. The agent runs the required tools on the backend and hands you a single, unified answer right where you are working. It turns a 10-minute multi-system chore into a two-second conversation.
Sift (Chargeback) MCP: Automated Fraud Decisioning
The key manual steps that disappear are the repetitive data pulls. You never have to copy scores from one screen and paste them into another, nor do you need to manually run through different tabs just to find a decision's timeline.
What’s different now is speed, visibility, and accuracy. You get real-time security intelligence directly embedded in your workflow, letting you act on fraud events the moment they happen—not when your morning report runs.
What Sift (Chargeback) MCP does for your AI
This connector gives your AI client full control over fraud prevention and chargeback management. Instead of jumping between dashboards to check risk or submit a dispute, you talk to your agent and it handles the heavy lifting. You can ask for a real-time fraud score on any user, instantly seeing if they're high risk or low risk.
Need to audit why an order was blocked? Ask the system to list all past decisions applied to that account. If something suspicious happens, you just report the chargeback event, and Sift updates the user profile immediately. Because Vinkius hosts this MCP, you connect your agent once, giving it access to powerful security tools like these for fraud intelligence, decision automation, and deep behavioral tracking.
019d756c-885a-72e3-abc5-4d7ca68160e8 How to set up Sift (Chargeback) MCP
The bottom line is you use natural language to perform complex security operations that usually require multiple logins and dashboard navigations.
Subscribe to this MCP and provide your specific Sift REST API Key and Account ID.
Authorize the connection within your preferred AI client (Claude, Cursor, etc.).
Ask your agent a question like, 'What is the fraud score for user X?' and get an immediate answer.
Who uses Sift (Chargeback) MCP
This connector is built for anyone whose job involves vetting transactions, managing payment fraud, or handling disputes. If your team spends time cross-referencing logs or manually checking user histories across different systems, this MCP saves you hours.
They use the tool to monitor current user scores and audit historical chargeback patterns using simple chat prompts.
They apply manual decisions (like blocking an account) or review decision history without ever leaving their main communication interface.
They verify transaction risk and report disputes for high-value orders directly from the chat window during peak hours.
Benefits of connecting Sift (Chargeback) MCP
Instant Risk Checks: Instead of logging into Sift just to see a score, you ask your agent for the get_user_fraud_score and get an immediate risk assessment in natural conversation. This cuts down on triage time drastically.
Full Audit Trail Access: You can request the full history of actions by running list_user_decision_history. Your team gets a clear, simple log of every decision made about a user’s account.
Proactive Fraud Logging: Use track_sift_event to automatically feed data—like new logins or transactions—back into Sift. This continuously sharpens their detection models without any manual effort from your side.
Immediate Dispute Reporting: When fraud hits, you simply run report_sift_chargeback. The agent handles the required data submission and ensures the user's risk profile is updated instantly for review.
Decision Enforcement: If a user needs to be blocked or their status changed, running apply_user_decision executes that action securely via chat, eliminating the need to click through complex UI forms.
Sift (Chargeback) MCP use cases
Handling an unknown fraud risk during checkout
A customer service agent receives a ticket for a high-value order. Instead of asking the customer for their account details and then opening Sift, they ask their agent: 'What is the fraud score for this user?' The agent runs get_user_fraud_score and sees it's 92% (Very High Risk). They immediately run apply_user_decision to block the order before processing.
Investigating a sudden spike in chargebacks
A risk analyst notices an unusual cluster of disputes. They ask their agent: 'List all recent chargeback events for this merchant.' The agent runs report_sift_chargeback and then uses list_user_decision_history to see if any past decisions correlate with the spike, finding a pattern they missed.
Training the model on new behavior
The development team needs to verify that Sift is tracking all relevant activity. They ask their agent: 'Log this specific login and transaction pair.' The agent executes track_sift_event, ensuring the data feeds directly into Sift for better future detection.
Auditing compliance after an incident
A manager needs to prove that all necessary steps were taken following a breach. They ask their agent: 'Show me every decision made on user X.' The agent compiles the report using list_user_decision_history, providing immediate, auditable proof.
Sift (Chargeback) MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Manual data aggregation
A user reads a fraud score in one dashboard, copies it into a spreadsheet, and then manually types the details into a ticket system to report the chargeback.
Don't copy anything. Use your agent to run get_user_fraud_score for the risk assessment, and then use report_sift_chargeback to submit the event directly from your chat interface.
Guessing required actions
A user tries to block a user but doesn't know the exact action name or parameters needed for Sift.
First, run list_sift_decisions to see all possible actions. Then, use your agent to execute that specific command via apply_user_decision.
Ignoring workflow visibility
A team suspects a process is broken but doesn't know which automated rules are running in Sift.
Use list_sift_workflows. This tool shows you exactly what fraud prevention pipelines are configured, giving you immediate oversight of the system.
When to use Sift (Chargeback) MCP
Use this MCP if your primary pain point is operating on critical security data (like user scores and dispute logs) but you hate context switching. You need to automate decision-making and reporting in a conversational way. This is ideal for Trust & Safety teams or analysts who operate across multiple systems. Don't use it if you are building an entirely new backend service; that requires direct API integration outside of your agent client. Also, don't rely on this MCP to replace dedicated BI tools—it reports data, but you still need those external tools for deep trend analysis and visualization. Use the get_user_fraud_score tool when you only need a single metric, but use the full conversation flow to handle complex tasks like reporting a chargeback or reviewing history.
Frequently asked questions about Sift (Chargeback) MCP
How do I check a user's risk score using the Sift (Chargeback) MCP? +
You ask your agent to run get_user_fraud_score and provide the username. The system returns the current numerical fraud score, along with any associated labels like $bad or $good.
Can I use Sift (Chargeback) MCP to block a user? +
Yes. You can execute an action by calling apply_user_decision and specifying the required decision, such as 'block_user,' which immediately updates their account status.
What if I need to update Sift after a dispute? +
You run report_sift_chargeback. This tool sends the official chargeback event details to Sift, ensuring that the user's risk profile is updated and reviewed by their models.
Does Sift (Chargeback) MCP track basic activity? +
It does. You can use track_sift_event to log specific events, like a transaction or login, directly into Sift for behavioral analysis and ML training.
How do I see what actions are available? (Sift (Chargeback) MCP) +
Run the list_sift_decisions tool. This shows you every recognized action or decision type that can be applied to a user within Sift.
Is it possible to check past decisions on a user? +
Absolutely. Use the list_user_decision_history tool, and the system will retrieve all historical records of actions taken against that specific user account.