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Core Concepts

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What is the Model Context Protocol?

The Model Context Protocol (MCP) is an open standard created by Anthropic that enables AI systems to securely connect with external tools, data sources, and APIs.

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Think of MCP as USB-C for AI

Just like USB-C provides a universal standard for connecting devices, MCP provides a universal standard for AI agents to connect with external tools and APIs. One protocol, any AI system, any API.

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MCP Architecture Flow

The MCP ecosystem consists of three key components working together: 
AI Host (Claude, ChatGPT, Custom Agent)
    ↓
MCP Client (Protocol Translator)
    ↓
MCP Server (Your API via Tydli)

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MCP Host (AI Application)

The AI application that wants to use external tools - like Claude Desktop, ChatGPT, or a custom AI agent you’ve built. Analogy: Think of the Host as the “boss” who needs to delegate specific tasks to specialized workers. The boss knows what needs to be done but doesn’t do the actual work. Examples:
  • Claude Desktop application
  • ChatGPT with custom plugins
  • Custom AI assistants built with LangChain
  • Continue.dev for VS Code

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MCP Client (Protocol Bridge)

The connector that speaks both languages - it translates between what the AI Host wants and what the MCP Server provides. Analogy: Like a translator at a business meeting. The Host speaks “AI language” and the Server speaks “API language.” The Client makes sure both sides understand each other perfectly. What it does:
  • Discovers available tools from MCP Servers
  • Translates AI requests into proper API calls
  • Handles authentication and security
  • Manages errors and retries

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MCP Server (Your API)

Your actual API wrapped in MCP protocol. This is what Tydli creates for you automatically from your OpenAPI spec. Analogy: The specialized worker who does the actual job. When asked to “get weather data,” it knows exactly how to fetch it from the weather API and return it in a format the AI can understand. Capabilities:
  • Tools: Functions that perform actions (e.g., “send_email”, “get_user”)
  • Resources: Data sources to read from (e.g., “user_database”, “file_storage”)
  • Prompts: Reusable templates (e.g., “professional_email_template”)

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Why MCP Changes Everything

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The Problem Before MCP

  • ✗ Each AI system had its own custom integration format
  • ✗ Building plugins required weeks of development time
  • ✗ APIs built for one AI didn’t work with others
  • ✗ Security and authentication were inconsistent
  • ✗ Maintaining multiple integrations was a nightmare

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The Solution With MCP

  • ✓ One standard protocol works across all AI systems
  • ✓ Deploy in seconds with tools like Tydli
  • ✓ Write once, use with Claude, ChatGPT, and more
  • ✓ Built-in security and authentication patterns
  • ✓ Automatic updates without breaking changes

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How Tydli Fits In

Tydli automatically generates MCP Servers from your existing OpenAPI specifications. This means:
  1. No code required - Your OpenAPI spec is all you need
  2. Instant deployment - Get an MCP server URL in seconds
  3. Production-ready - Built-in authentication, rate limiting, and error handling
  4. MCP compliant - Follows the official Model Context Protocol 2025-03-26 specification

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Next Steps