Advanced7 min

MCP Servers

Model Context Protocol (MCP) servers extend AI capabilities by providing custom tools. Build tools that AI assistants can use.

What is MCP?

MCP (Model Context Protocol) is a standard for:

  • Exposing tools to AI models
  • Providing context and data
  • Enabling AI-powered actions

Use Cases

  • Connect AI to your APIs
  • Create custom AI tools
  • Build AI-powered widgets
  • Extend AI assistants

How MCP Works

Architecture

AI Model ←→ MCP Server ←→ Your API/Service
  1. AI model requests a tool
  2. MCP server handles the request
  3. Executes against your service
  4. Returns results to AI

Tools

Tools are functions the AI can call:

  • Defined with name and description
  • Take structured parameters
  • Return structured results

Example tool:

{
  name: "get_weather",
  description: "Get current weather for a location",
  parameters: {
    location: { type: "string", required: true }
  }
}

Using MCP Servers

Installing from Marketplace

  1. Go to Marketplace > MCPs
  2. Find an MCP server
  3. Click Install
  4. Configure credentials

Connecting to Workflows

MCP tools can be used in workflows:

  1. Add an MCP Action node
  2. Select the installed MCP
  3. Choose the tool
  4. Configure parameters

Using with AI Chat

MCPs enhance AI assistants:

  1. Install MCP server
  2. Enable for chat
  3. AI automatically uses available tools

Built-in MCPs

Database MCP

Query your databases via AI:

  • Natural language queries
  • Automatic SQL generation
  • Safe read-only by default

API MCP

Expose your APIs to AI:

  • Import from OpenAPI
  • AI can call endpoints
  • Results formatted for AI

File MCP

File operations:

  • Read files
  • Search content
  • Generate reports

Managing MCPs

View Installed

  1. Go to Settings > MCPs
  2. See all installed servers
  3. View status and usage

Configuration

Each MCP may need:

  • API credentials
  • Connection settings
  • Permission scopes

Permissions

Control what MCPs can do:

  • Read-only vs read-write
  • Allowed operations
  • Data access limits

Monitoring

Track MCP usage:

  • Tool invocations
  • Success/failure rates
  • Response times

Security Considerations

Authentication

  • MCPs use secure credentials
  • Never expose secrets to AI
  • Use scoped API keys

Data Access

  • Limit to necessary data
  • Use read-only when possible
  • Audit access logs

Sandboxing

  • MCPs run in isolation
  • Cannot access other resources
  • Rate limited by default

Creating MCPs

Build your own MCP servers:

  1. Define your tools
  2. Implement handlers
  3. Deploy the server
  4. Register with the platform

See Building MCP Servers for details.

Widgets

MCPs can power embeddable widgets:

  • Chat interfaces
  • Data displays
  • Interactive tools

See MCP Widgets for details.

Next Steps

Tags

mcpaiadvanced