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What is MCP?

The Model Context Protocol (MCP) is an open standard that enables AI agents to securely connect to external tools, data sources, and services. Think of it as a universal translator between AI models and the real world.

The problem MCP solves

Before MCP, connecting AI agents to external systems required building custom integrations for each combination of agent and tool. This meant:

  • M × N integration complexity: Every AI agent needed custom code for every tool it wanted to use.
  • A fragmented ecosystem: There was no standardized way for tools to expose their capabilities to AI agents.
  • Security challenges: Each integration handled permissions and access differently.
  • More maintenance overhead: Updates to any system broke multiple custom integrations.

A diagram shows multiple AI agents (Claude Desktop, VS Code, and Cursor), which each require separate custom integrations to connect to different tools (GitHub, PostgreSQL, and the user's file system) creating a complex web of M × N connections between agents and services.

In the diagram above, we see how each application (Claude Desktop, VS Code, Cursor) requires custom integrations to access various tools (GitHub, PostgreSQL, the user’s file system). This creates a complex web of M × N connections that is difficult to maintain and scale.

With MCP, we introduce a standardized protocol that simplifies this process.

There is now a single integration point for each tool, allowing any compatible AI agent to access it without custom code.

A diagram shows a simplified architecture with multiple AI agents (Claude Desktop, VS Code, and Cursor) connected through a single MCP layer to multiple tools (GitHub, PostgreSQL, the user's file system, and services). This demonstrates how MCP reduces complexity from M × N custom integrations to M + N standardized connections.

In the diagram above, we see how MCP simplifies the architecture. Each AI agent (Claude Desktop, VS Code, Cursor) connects through a single MCP layer to access multiple tools (GitHub, PostgreSQL, the user’s file system).

How MCP works

MCP establishes a client-server architecture made up of:

  • MCP clients: AI agents, applications, or development tools (like Claude Desktop, VS Code extensions, and Cursor)
  • MCP servers: Services that expose tools, data, or capabilities (like GitHub, databases, and APIs)
  • A standardized protocol: A common language both clients and servers understand

Instead of building custom bridges, you now have:

  • M + N simplicity: One MCP integration per tool, which works with all compatible AI agents
  • Standardized security: Consistent authentication and authorization patterns
  • Easy maintenance: Protocol updates that benefit the entire ecosystem

Key benefits of MCP

Depending on your role in the ecosystem, MCP provides several advantages.

Advantages for AI agent users

When using MCP-compatible AI agents, like Claude Desktop, VS Code extensions, or Cursor, you get:

  • Access to more tools and data sources without custom integrations
  • A consistent experience interaction pattern across different AI agents
  • Better security through standardized authentication and authorization

Advantages for tool providers

When you build an MCP server to expose your tools, you gain:

  • A single integration point for all compatible AI agents
  • More users for your tools without custom integration work
  • Capacity to focus on your core product instead of on AI integrations

Advantages for agent developers

When you build MCP-compatible applications or agents, you benefit from:

  • Access to a growing ecosystem of tools and services
  • Simplified development with standardized protocols
  • Increased collaboration opportunities across different AI agents

Real-world example of MCP in action

See how MCP simplifies the process of connecting AI agents to external tools and data sources.

Imagine you want to use Claude Desktop to read a GitHub issue from your company’s private repository and query your PostgreSQL database for related data.

The manual process

If this were a once-off task, you would typically need to:

  1. Copy and paste the contents of the GitHub issue into Claude Desktop.
  2. Describe the database schema to Claude.
  3. Ask Claude to write a custom query based on the pasted schema.
  4. Copy and paste the query into your database client.
  5. Copy and paste the results back into Claude Desktop.
  6. Manually combine the information from the GitHub issue and database query results.

The custom integration process

If you did this often, you might write a custom integration that connects Claude Desktop to GitHub and PostgreSQL, which would require ongoing maintenance and updates. The steps involved are cumbersome and error-prone, especially if the GitHub issue or database schema changes. This would get even more complex if Claude needed to wait for long-running queries or if you wanted to access multiple tools at once.

The MCP way

With MCP, you would:

  1. Install the GitHub MCP Server  and a PostgreSQL MCP server .
  2. Ask Claude: “Fetch issue #123 from the my-org/my-repo repository and query the my_table table to find related data. Interpret the results and summarize the information.”

This process is much simpler, more secure, and less error-prone. You can also easily switch to a different AI agent that supports MCP without needing to rewrite your integrations.

How to get started with MCP

Ready to start using MCP? Here are your next steps for:

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