Model Context Protocol (MCP) Directory

Model Context Protocol (MCP): Bridging LLMs and External Resources

The Model Context Protocol (MCP) is an innovative open protocol designed to standardize how Large Language Models (LLMs) interact with external data sources and tools. Developed to overcome the knowledge limitations of LLMs, MCP acts as a universal connector, much like a "USB-C port for AI applications," enabling seamless integration between AI models and various external resources.

Key Components of MCP

MCP Servers

MCP Servers are responsible for managing specific data sources or tools and exposing them to LLMs through the protocol. They can:

• Provide access to file systems, APIs, or proprietary datasets
• Offer specialized capabilities through tools, resources, and prompts
• Run locally on a user's device or be deployed to remote servers

MCP Clients

MCP Clients serve as the bridge between LLMs and MCP Servers. Their primary functions include:

• Maintaining one-to-one stateful connections with servers
• Routing messages between hosts and servers
• Managing capabilities and protocol negotiations
• Handling subscription management for server resources

MCP Hosts

MCP Hosts are applications that integrate LLMs and Clients, such as Claude Desktop, Cursor IDE, or Windsurf IDE. They are responsible for:

• Initializing and managing multiple clients
• Handling user authorization decisions
• Managing context aggregation across clients

How MCP Works

The MCP workflow typically follows these steps:

1. An MCP Host initiates a request to an LLM.
2. The LLM, through its embedded MCP Client, determines it needs external data or tools.
3. The MCP Client forwards the request to the appropriate MCP Server.
4. The MCP Server retrieves the necessary information or executes the required tool.
5. Results are returned to the LLM via the MCP Client.
6. The LLM incorporates the new context into its response.

Advantages of MCP

• Standardization: MCP provides a uniform method for connecting AI systems to various tools and data sources, simplifying integration processes.
• Enhanced Context: By enabling LLMs to access up-to-date information and specialized tools, MCP helps produce more relevant and accurate responses.
• Scalability: MCP allows developers to create standalone servers that can be reused across multiple LLM applications, reducing redundancy in integration efforts.
• Security: The protocol's architecture ensures that models access only necessary data, enhancing both security and performance.
• Flexibility: MCP supports a wide range of use cases, from simple data retrieval to complex, stateful interactions between LLMs and applications.

MCP vs. Traditional APIs

While MCP may seem similar to conventional APIs, it offers several distinct advantages:

• Stateful connections, unlike stateless REST APIs
• A standardized protocol for context provision to LLMs
• Support for complex workflows involving deep interactions between LLMs and applications

Explain It Like I'm 5

Imagine you have a super smart robot friend who can answer lots of questions. But sometimes, your robot friend doesn't know everything and needs help from other friends who have more information.

MCP is like a special messenger that helps your robot friend talk to its other friends who have more information. These friends are like libraries or toolboxes that can help your robot friend answer questions better.

Here's how it works:

1. Your robot friend says, "I need help with this question."
2. The messenger (MCP Client) goes to the right friend (MCP Server) who has the answer.
3. The friend gives the answer to the messenger.
4. The messenger brings the answer back to your robot friend.
5. Now, your robot friend can give you a better answer because it got help from its friends!

This way, MCP helps make your robot friend (or AI) smarter by connecting it to lots of helpful friends who have more information.

Learn more about MCP