Model Context Protocol (MCP)
What is the Model Context Protocol (MCP)?
AI assistants, especially Large Language Models (LLMs) like ChatGPT and Claude, are powerful because they can understand natural language, generate human-like responses, summarize information, write content, analyze data, and support decision-making. However, despite their intelligence and conversational abilities, they cannot directly interact with live business systems, databases, or applications on their own.
To bridge this gap, Anthropic developed the Model Context Protocol (MCP), an open-source standard designed to enable secure and standardized communication between AI assistants and external tools, applications, and data sources.
By providing a consistent integration framework, MCP allows AI systems to move beyond conversational capabilities and interact more effectively with real-world business environments.
Using MCP, AI assistants can interact with:
- Business productivity applications.
- Local files and databases.
- External workflows and APIs.
- Systems with predefined prompts.
MCP acts as a communication bridge between AI assistants and external tools, databases and services, helping AI systems access information and perform tasks more efficiently.
Why is MCP needed?
Having a standardized protocol like MCP offers the following benefits.
It eliminates custom integration efforts
Before MCP, developers had to build separate integrations for every AI assistant and every external tool. This created unnecessary complexity and repetitive engineering work.
MCP provides a common protocol that allows AI assistants to connect with different applications through a standard framework. This removes the need to create custom integrations for each AI assistant.
You can access live data from your own systems
MCP enables AI assistants to connect with MCP servers that can dynamically fetch live data from your own systems and environments such as the local file system, GitHub repositories, or your organization’s databases. When connected to accurate and up-to-date sources, this can significantly improve the relevance and reliability of AI-generated responses.
It enables cross-system workflows
MCP allows AI systems not only to retrieve information but also perform actions across applications. For example, an AI assistant could read a task from Zoho Projects, generate a summary document, save the document to Zoho WorkDrive, and notify a team through Zoho Cliq.
It offers better context and accuracy
AI assistants produce better results when they have access to the right context. MCP helps assistants gather relevant business information from connected systems before generating responses. This reduces hallucinations and improves response accuracy.
It provides security and controlled access
MCP also provides a structured way to manage permissions and access to tools. Organizations can control what data the assistant can access and what actions it can take.
MCP architecture explained
MCP follows a client-server architecture consisting of three main components.
MCP host
The MCP host is the main AI application that users interact with. It acts as the central controller in the MCP architecture and can connect to multiple MCP servers simultaneously.
Examples include:
- AI chat applications.
- IDE assistants.
- Desktop AI tools.
MCP client
The MCP client manages communication between the MCP host and MCP servers. For every MCP server connection, the host creates a separate client connection to keep communication secure and organized.
Its responsibilities include:
- Relaying tool discovery requests to the MCP server.
- Sending structured requests to the appropriate server.
- Receiving and returning responses to the host.
MCP server
MCP servers provide external capabilities to the AI system. They connect the AI assistant with external tools, databases, APIs, and applications.
An MCP server can:
- Retrieve information from external sources.
- Access files or databases.
- Perform actions in external applications.
- Return structured responses to the MCP client.
How does MCP work?
Here’s a step-by-step walkthrough of how the MCP host, client, and server work together to complete a task.
- User sends a request: The user asks the AI assistant to answer a question or complete a task.
- Tool discovery: The AI assistant determines whether it needs external data or tools to complete the request. It checks with the MCP client for the list of available tools and their capabilities.
- Tool selection and structured request: Based on the available tools, the AI assistant selects the appropriate tool and passes a structured request, containing the tool name and required inputs to the MCP client, which forwards it to the corresponding MCP server.
- External system access: The MCP server securely connects with the required application, database, or service.
- Data or action return: The MCP server retrieves the information or performs the requested action, then sends the result back to the MCP client.
- Response generation: The MCP client returns the result to the AI assistant.
- Final response: The AI assistant uses the result to formulate a response and answers the user or confirms that the requested task has been completed.
Get started with Zoho Mail MCP
Zoho Mail’s MCP server enables AI assistants to securely connect with your Zoho Mail account, allowing them to read, compose, send, and manage emails through a standardized MCP connection.
To get started, explore the Zoho Mail MCP Getting Started guide, Zoho Calendar MCP Getting Started guide to learn how to use AI agents to manage your inbox using Zoho MCP.