Model Context Protocols (MCPs) have changed the way AI agents access data, find information, and complete various tasks. It’s easy to play with AI models, but if you’re looking to make a real impact on your workflow, you need to learn exactly what happens inside an .
The MCP Ecosystem
To understand what an MCP server is, you have to understand the entire . The three components of MCPs are:
- The Host - the LLM (large language model)
- The Client - the communicator between the host and the server
- The Server - the implementation of the protocol
These three components make up the MCP ecosystem and allow you to integrate multiple data sources into your AI agent.
MCP Servers?
The MCP server's main goal is to gather data, complete a task, and then send the output back to the client. To help you visualize what an MCP server can do and how it works, you can split up its capabilities into five main components:
- Communication Layer
- Request Execution Handlers
- Context Warehouse
- Session Organizer
- Caching Layers
Splitting up the idea of MCP servers allows us to better understand how they work as a whole.
Communication Layer: You can also think of this as the protocol layer. This is the part of an MCP server that takes requests, translates them into a language the rest of the server understands, and delivers responses back to the client once the server has completed the task. It ensures that the client and server are on the same page.
Request Execution Handlers: Think of these as the portion that executes tasks like:
- Retrieving information
- Sending emails
- Creating records
- Managing workflows
- And more!
When a request comes in, the server's communication layer routes it to the appropriate handler, which has its own command.
Context Warehouse: Think of this as the storage that allows MCP servers to remember information in between queries. This can include in-memory storage, databases, or external services, and it allows the server to complete multi-step tasks.
Session Organizer: With so much data passing through MCP servers, it needs to stay organized. The session organizer aspect of an MCP server helps orchestrate everything in the back end. They maintain session data and link history while closing old sessions that aren't needed anymore.
Caching Layers: Another important functionality of MCP servers is the ability to cache information. Caching layers temporarily store frequently accessed data to improve performance, especially when searching through large databases for information. These layers may also cache information that would be otherwise expensive to keep retrieving.
Utilizing MCP Servers for AI
Adopting the use of MCP servers while you integrate AI into your workflow allows for:
- Integration Flexibility - Connect all your applications for easy data access
- Simplification of Workflows - Encourage communication between your apps and minimize complex manual tasks
- Scalability - Utilize the same MCP server frameworks as your company grows and you adopt more platforms
To start utilizing MCPs to make your AI Agent work better for your team, find an MCP server designed with data integration and data security in mind.
MCP Server Integration
Learning how MCP servers work opens up a world of possibilities about how your business can adopt AI functionality. Take advantage of AI within your workflows with . PopdockAI's MCP server allows you to:
- Connect any AI platform of your choice with ease
- Give your AI access to the data it needs to succeed
- Get answers powered by your secure, clean, and up-to-date data
Have questions about PopdockAI's MCP server capabilities? Please team to learn more.