Integrating MCP

Model Context Protocol (MCP) is an open standard that enables seamless communication between AI agents and external tools. When you implement MCP in your AI agent or tool, customers can integrate your solution directly into their existing agentic workflows without complex API integration work.

MCP transforms how AI agents access external capabilities. Instead of building custom integrations for each tool, agents use a standardized protocol to discover, connect, and interact with MCP-compatible services. This approach reduces integration complexity and enables plug-and-play functionality.

For more information about implementing Model Context Protocol in your agent or tool, see Integrating MCP.

Key Benefits of Model Context Protocol integration

MCP integration provides advantages for both AI agent providers and end users.

Benefits for AI agent providers

• Reach customers using popular AI development solutions that support MCP.

• Reduce customer onboarding friction with standardized integration.

• Enable discovery through MCP-compatible client applications.

• Support multiple client platforms with a single implementation.

Benefits for end users

• Add your capabilities to existing AI workflows without custom development.

• Use familiar Model Context Protocol client interfaces they already know.

• Benefit from automatic protocol handling and error management.

• Access your tools through multiple AI platforms and applications.

• Maintain consistent authentication across Model Context Protocol-enabled services.

Model Context Protocol Architecture and components

Model Context Protocol uses a client-server architecture where your AI agent or tool acts as an Model Context Protocol server. Customer applications (Model Context Protocol clients) connect to your server to access your capabilities.

The protocol defines the following three main types of capabilities:

• Tools – Functions that agents can call to perform actions.

• Resources – Data sources that agents can read or query.

• Prompts – Pre-defined prompt templates that agents can use.

Technical requirements for Model Context Protocol implementation

Your Model Context Protocol server must implement the following core protocol specifications:

• JSON-RPC 2.0 communication protocol

• Standard Model Context Protocol message types and formats

• Capability advertisement and discovery

• Authentication and session management

• Error handling and status reporting

Implementation steps

1. Define your capabilities (tools, resources, or prompts).

2. Implement the Model Context Protocol server interface.

3. Create capability schemas using JSON Schema.

4. Implement authentication and authorization.

5. Add error handling and logging.

6. Test with Model Context Protocol-compatible clients.

7. Document your capabilities for customers.

Example capability definition

{
  "name": "search_knowledge_base",
  "description": "Search the knowledge base for relevant information",
  "parameters": {
    "type": "object",
    "properties": {
      "query": {
        "type": "string",
        "description": "The search query"
      },
      "max_results": {
        "type": "integer",
        "description": "Maximum number of results to return",
        "default": 5
      }
    },
    "required": ["query"]
  }
}

Testing and validation

Before listing your Model Context Protocol-compatible agent or tool on AWS Marketplace, thoroughly test your implementation:

• Verify capability discovery and schema validation

• Test authentication flows and error handling

• Validate performance under various load conditions

• Ensure compatibility with popular Model Context Protocol clients

• Document any client-specific configuration requirements

Documentation requirements

When listing an Model Context Protocol-compatible agent or tool on AWS Marketplace, include comprehensive documentation:

• Detailed capability descriptions and examples

• Authentication and configuration instructions

• Sample code for common integration scenarios

• Troubleshooting guides and error reference

• Performance considerations and best practices

Additional resources

For more information about implementing Model Context Protocol in your AI agent or tool, refer to these resources:

• Model Context Protocol Specification

• MCP GitHub Repository

• AWS Marketplace Seller Operations team for Model Context Protocol integration support