Model Context Protocol Features

Core Features

Structured Context Schema

Define your context structure once, use it consistently across your entire AI application. MCP provides a standardized way to organize all the information that guides an LLM's behavior.

// Define your MCP schema
const mcpSchema = {
  version: "1.0",
  components: {
    systemInstruction: {
      type: "string",
      description: "High-level instruction for the LLM"
    },
    userGoal: {
      type: "string",
      description: "Current user objective"
    },
    // More components...
  }
};

Provider Agnostic

Works with any LLM provider including OpenAI, Anthropic, Google, and more. Create your context once, then compile it for your specific LLM implementation.

// Compile for different providers
const openaiPrompt = assistant.compileFor('openai');
const anthropicPrompt = assistant.compileFor('anthropic');
const googlePrompt = assistant.compileFor('google');

Memory Management

Structured approach to short and long-term memory in your LLM applications. Maintain conversation context and user preferences over time.

// Memory management
assistant.updateMemory({
  shortTerm: [
    { type: "interaction", content: "Last user query and response" }
  ],
  longTerm: {
    userPreferences: { theme: "dark", detailedResponses: true }
  }
});

Tool Integration

Standardized way to define and use tools with LLMs. Connect your AI to external APIs, databases, and services.

// Tool definition
const tools = [
  {
    name: "searchDatabase",
    description: "Search the product database",
    parameters: {
      query: "string",
      limit: "number"
    }
  },
  // More tools...
];

Observability

Debug and trace what influenced model outputs. Understand exactly why your LLM responded the way it did.

// Logging and tracing
const trace = assistant.getContextTrace();
console.log("Context that influenced response:", trace);
// Logs all components that affected the model's output

Advanced Features

Multi-Agent Coordination

Coordinate multiple AI agents with shared context. Build complex workflows where agents collaborate on tasks.

Retrieval-Augmented Generation (RAG)

Structured integration with knowledge bases and document retrieval. Provide relevant information to your LLM in a consistent format.

Context Versioning

Track changes to your context schema over time. Maintain backward compatibility as your AI application evolves.

Personalization Framework

Build user-specific context profiles. Deliver tailored experiences based on preferences, history, and behavior.

Framework Integration

Works with popular AI frameworks like LangChain, LlamaIndex, and custom solutions. Integrate MCP into your existing stack.

Use Cases

E-commerce Assistants

Build shopping assistants that remember preferences, compare products, and make personalized recommendations.

Customer Support

Create support agents that access knowledge bases, user history, and support tools to resolve issues efficiently.

Developer Copilots

Build coding assistants that understand your codebase, development patterns, and can access APIs and documentation.

Content Creation

Develop content assistants with brand guidelines, style preferences, and access to reference materials.