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The Model Context Protocol (MCP) is an open protocol that enables AI models to securely access external data sources, tools, and resources through a standardized interface. Adaline’s Editor lets you connect to remote MCP servers and use their tools directly in your prompts — enabling powerful integrations without custom backend code.

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Enable MCP

1

Select a compatible model

Open the model selector and choose an LLM that supports MCP integration.
MCP integration currently requires selecting an Anthropic model. If you need MCP support for other providers, contact support@adaline.ai for a private preview.
2

Open MCP settings

In the model settings panel, select MCP.Selecting MCP in Adaline
3

Toggle MCP on

Set the MCP toggle to On to enable the integration.Enabling MCP in Adaline
Once enabled, the Editor displays the MCP configuration panel where you can add your server settings: MCP integration ready in Adaline

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Configure an MCP server

Each MCP server requires a JSON configuration object. Here is the complete schema: 
{
  "type": "url",
  "url": "https://example-server.modelcontextprotocol.io/sse",
  "name": "example-mcp",
  "tool_configuration": {
    "enabled": true,
    "allowed_tools": ["example_tool_1", "example_tool_2"]
  },
  "authorization_token": "YOUR_TOKEN"
}

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Configuration reference

FieldTypeRequiredDescription
typeStringYesCurrently only "url" is supported.
urlStringYesThe MCP server URL. Must start with https://.
nameStringYesA unique identifier for the server. Choose a descriptive name.
tool_configurationObjectNoControls which tools from the server are available.
tool_configuration.enabledBooleanNoWhether to enable tools from the server (default: true).
tool_configuration.allowed_toolsArrayNoRestrict which tools are allowed. By default, all tools are available.
authorization_tokenStringNoOAuth authorization token, if required by the server.

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Connect to multiple servers

You can connect to multiple MCP servers simultaneously by providing an array of server configurations: 
[
  {
    "type": "url",
    "url": "https://mcp.example1.com/sse",
    "name": "mcp-server-1",
    "authorization_token": "YOUR_TOKEN_1"
  },
  {
    "type": "url",
    "url": "https://mcp.example2.com/sse",
    "name": "mcp-server-2",
    "authorization_token": "YOUR_TOKEN_2"
  }
]
Each server’s tools become available to the model independently. The model can choose which server’s tools to invoke based on the conversation context.

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Authentication

Many MCP servers require OAuth authentication. To obtain an access token:
  1. Use the MCP Inspector or your server provider’s OAuth flow to generate a token.
  2. Paste the token into the authorization_token field in your server configuration.
Keep your authorization tokens secure. If your MCP server’s tokens have an expiry, you will need to refresh them periodically.

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Next steps

Use Tools in Prompts

Define custom tools with JSON schemas and HTTP backends.

Tool Calls in Playground

Test MCP-powered and tool-powered prompts in the Playground.