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What is the Model Context Protocol?

The Model Context Protocol (MCP) is an open standard that allows AI assistants — such as Claude, Cursor, or any MCP-compatible agent framework — to call external tools and services at runtime. Think of it as a structured bridge between a language model and the APIs it needs to interact with. An MCP server is a lightweight service that exposes a set of named tools to an AI client. When an AI assistant is connected to an MCP server, it can invoke those tools autonomously: decide when to call them, pass the right parameters, and interpret the results — all without requiring you to build a custom integration from scratch. The identifAI MCP Server wraps the entire identifAI REST API and exposes it as a collection of ready-to-use tools that any MCP-compatible client can discover and call.
Since the MCP server is remote and the protocol is JSON-based, all file samples are transferred as base64-encoded strings. This can cause issues with large files (especially videos), as base64 encoding increases the payload size and may hit server or client limits. For large files, it is strongly recommended to use the “by URL” classification tools (e.g., classify_image_url, classify_video_url, classify_audio_url) to avoid transfer problems and ensure reliable processing.

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Why use identifAI over MCP?

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For AI-native workflows

If you are building or deploying AI agents, chatbots, or copilots, MCP integration is the fastest path to equipping them with AI-content detection capabilities. Rather than writing API glue code, you describe a task in natural language and the agent handles the rest:
“Check whether this image is AI-generated before publishing it.” “Analyse this audio clip and tell me if it’s synthetic.”

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Common use cases

Content Moderation

Block the publication of AI-generated images, videos, or audio on social platforms, forums, marketplaces, and online communities.

Fact-Checking & Newsroom

Verify the authenticity of photos, videos, and audio in newsrooms, press agencies, and source verification workflows.

Identity & KYC

Detect deepfakes and manipulations in onboarding, identity verification, and KYC processes for banks, insurance, and fintech.

Copyright & Brand Protection

Protect intellectual property and copyright by detecting AI content in advertising campaigns, digital assets, and media archives.

Ad Integrity & Anti-Fraud

Prevent ad fraud and fake ads by detecting AI-generated content in marketing campaigns and advertising networks.

Broadcast & Media

Safeguard the reputation of broadcasters, TV, and radio by verifying the genuineness of content before airing.

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Advantages over a raw REST integration

REST APIMCP Server
Integration effortCustom HTTP client codeZero glue code — just add to your agent config
Agent-friendlyRequires custom tool wrappingNatively discoverable by MCP clients
Multi-modalSeparate endpoints per typeAll 17 tools in a single server
Async pollingManual polling loopTool handles the workflow

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Requirements

Before connecting to the identifAI MCP server you need:
  1. An identifAI API key. Register at https://web.identifai.net, then go to the dashboard, click the user icon in the top-right corner, and select “Api-Key”.
  2. An MCP-compatible client — Claude Desktop, Cursor, or any framework agent that supports the Model Context Protocol.
Your API key is passed per request as a tool parameter. The server never stores or caches it — each tool call authenticates independently directly against the identifAI REST API.

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Public MCP server endpoint

For production use, you can connect directly to our public MCP server at:
https://mcp.identifai.net
This endpoint is always available and ready to serve requests from any MCP-compatible agent or assistant. No local deployment is required unless you want a private instance. Simply configure your agent to use https://mcp.identifai.net as the MCP server URL.

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Available tools

The server exposes 17 tools organised by media type.
ToolDescription
identifai/classify_imageSubmit an image file for AI-content classification
identifai/classify_image_urlSubmit an image URL for AI-content classification
identifai/get_image_classificationRetrieve the result of a classification by ID
identifai/get_all_image_classificationsRetrieve multiple image classification results
identifai/get_classification_heatmapGet a heatmap highlighting AI-generated regions
identifai/override_image_classificationManually override a classification result
ToolDescription
identifai/classify_videoSubmit a video file for AI-content classification
identifai/classify_video_urlSubmit a video URL for AI-content classification
identifai/get_video_classificationRetrieve the result of a video classification by ID
identifai/get_all_video_classificationsRetrieve multiple video classification results
identifai/override_video_classificationManually override a video classification result
ToolDescription
identifai/classify_audioSubmit an audio file for AI-content classification
identifai/classify_audio_urlSubmit an audio URL for AI-content classification
identifai/get_audio_classificationRetrieve the result of an audio classification by ID
identifai/get_all_audio_classificationsRetrieve multiple audio classification results
identifai/override_audio_classificationManually override an audio classification result
ToolDescription
identifai/get_user_creditsRetrieve the remaining credit balance for your account
Classification jobs are asynchronous. After submitting a file or URL, use the corresponding get_*_classification tool with the returned job ID to poll for the result. MCP-compatible agents handle this automatically when instructed to wait for completion.

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Integration example: Claude Desktop

The quickest way to get started is to connect Claude Desktop directly to the public identifAI MCP server — no local deployment required.
  1. Open (or create) the Claude Desktop configuration file:
    • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows: %APPDATA%\Claude\claude_desktop_config.json
  2. Add the identifAI server to the mcpServers section:
{
  "mcpServers": {
    "identifAI": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://mcp.identifai.net/"]
    }
  }
}
Make sure you have npx installed (included with Node.js) to start the mcp-remote server via command.
  1. Restart Claude Desktop. You should now see identifAI listed in the connected tools panel.
  2. In the Claude Desktop chat, try a prompt like:
“Using identifAI with API key sk-xxxxxxxx, classify this image and tell me if it’s AI-generated: https://example.com/sample.jpg”
Claude will call identifai/classify_image_url, receive the job ID, then automatically poll identifai/get_image_classification and return the verdict with its confidence score.
You can include your API key directly in the prompt, or ask the assistant to use a key you have stored in a system prompt or environment variable — depending on how your agent is configured.

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Next steps

Get your API key

Follow the quickstart guide to create an account and retrieve your API key.

Explore the API Reference

Browse the full REST API documentation for all available endpoints.

Classify an image

Learn how image classification works end-to-end.

Classify a video

Learn how video classification works end-to-end.