Model Context Protocol (MCP)
Connect the ComplyCube MCP server to Claude, Cursor, ChatGPT, and other AI assistants for grounded access to ComplyCube documentation and integration guides.
Overview
Use the ComplyCube Model Context Protocol (MCP) server to connect Claude, Cursor, ChatGPT, Gemini, Perplexity, VS Code extensions, Windsurf, and other MCP-compatible AI assistants to ComplyCube documentation.
Use it to search ComplyCube docs, read full pages, and generate code grounded in current ComplyCube guidance.
This guide explains how ComplyCube MCP works, how to connect it, and how to use it for API, SDK, workflow, and webhook integrations.
The ComplyCube MCP server provides read-only access to publicly available documentation. It cannot view or interact with your API keys, Portal account, client records, or verification data.
What is MCP?
Model Context Protocol is an open standard that lets AI assistants connect to external tools and data sources.
The ComplyCube MCP server gives your AI assistant access to this documentation. Once connected, the assistant can search the docs and read full pages while it answers questions or drafts code.
This helps your assistant use current ComplyCube documentation instead of relying only on older training data.
The ComplyCube MCP server is read-only. It only serves documentation. It does not have access to your API keys, Portal, clients, checks, cases, or Sandbox and Live account data.
Use the MCP server to give your AI assistant access to ComplyCube documentation. It provides product knowledge and API context, while your application and backend continue to handle business logic and verification workflows.
Works with every ComplyCube product
The MCP server covers the full ComplyCube documentation, so you can use it across the platform.
Verification checks
Ask about specific checks and verification flows:
Extensive AML screening for sanctions, PEPs, and adverse media checks.
Document checks for document authenticity and data extraction.
Identity checks for liveness and biometric face match.
Proof of address checks for address document validation.
Multi-bureau checks for bureau and trusted data-source matching.
Integration paths
Use MCP to compare integration options and implementation details:
Hosted Solution for a fully hosted flow.
Web SDK and Mobile SDKs for embedded experiences.
API Reference for an API integrations with granular controls.
Developer tools and operations
Use MCP to find exact implementation details fast:
Webhooks for event handling and async updates.
Postman and testing data for sandbox testing and debugging.
Compliance guidance for trust, controls, and privacy coverage.
You can ask your AI assistant about any of these areas and it will search the right pages for you.
What you can do
Search the docs in plain language.
Read full pages when a snippet is not enough.
Draft integration code grounded in current ComplyCube guides.
Debug headers, webhook payloads, and parameter names without leaving your editor.
In most AI clients, you do not need to call these directly. Ask your question normally and let the assistant choose when to search or read.
Connect your AI assistant to the ComplyCube MCP server
You do not need to install anything locally. The ComplyCube MCP server runs online.
Copy this URL into your MCP client:
Most MCP-compatible clients let you add a remote server from their settings or connector menu.
Use these connection details:
Name: ComplyCube Docs
Authentication: None
Then:
Open your AI assistant's MCP, connectors, or integrations settings.
Add a new remote MCP server.
Enter the ComplyCube Docs name and URL.
Save the connection.
Enable the server in the workspace or chat where you want to use it.
MCP setup steps vary by client and change often. If your assistant supports remote MCP servers, use the server URL above and follow that client's current setup flow.
How to use the ComplyCube MCP server
After you connect the server, ask your assistant questions in plain language. You do not need to call the documentation tools yourself.
Find information in ComplyCube docs
Try prompts like:
"How do I create a client and run a document check in Sandbox?"
"Which ComplyCube integration path fits a hosted onboarding flow with minimal code? Use the Hosted Solution Guide."
"Which webhook events should I handle after a check completes? Use the Webhooks Guide."
Generate ComplyCube integration code
Ask for code grounded in current docs, for example:
"Write Node.js code that creates a client and starts a hosted verification flow."
"Write a Python example that creates a check and retrieves the result."
For concrete implementation patterns, start with the API Quick Guide, Hosted Solution Guide, Web SDK Guide, or Mobile SDK Guide.
Debug your ComplyCube integration
Use the MCP server to inspect exact field names and flows:
"Why does my webhook signature validation fail?"
"Which parameters are required when I generate an SDK token?"
Best practices for better prompts
State whether you want a workflow-based or check-based integration path.
Be specific about whether you use Hosted Solution, Web SDK, Mobile SDKs, or direct API integration.
Ask for exact endpoint names, payload fields, and sequence steps.
Keep API keys, secrets, and personal data out of prompts.
Search first, then ask for full-page detail when needed.
Always review generated code before you ship it.
Privacy and scope
The MCP server only serves content from ComplyCube documentation.
It does not have access to your portal, sessions, API keys, or customer data.
Your prompts and the returned documentation flow through your AI assistant provider. Check your provider's data policy before sharing sensitive information.
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