Understanding the MCP protocol in one article: making AI understand your data better
What is MCP?
What is MCP
The Model Context Protocol (MCP) is a machine learning modeling service designed specifically forcommunications protocol, which aims to efficiently manage contextual information (e.g., session state, environment variables, dynamic configuration, etc.) during model inference, addressing the limitations of traditional protocols in complex AI scenarios.
MCP doesn't handle complex logic per se; it simply coordinates the flow of data and instructions between the AI model and the tool, and MCP simplifies the way the AI model interacts with your data, tools, and services.
MCP, what exactly does it do?
1) Context-aware reasoning
Support for dynamically passing and persisting session-related contextual data (e.g., user history behavior, conversation state) enables the model to generate coherent results based on continuous interactions. For example, in a chatbot scenario, the MCP passes the conversation history to ensure the contextual relevance of the answers.
In layman's terms: For example, if a customer service bot knows you've returned an item before, it doesn't have to ask you for your order number every time. Assign a "session ID" to each conversation, which can be used to store chat logs, user preferences, and so on.
2) Multi-modal data compatibility
Unified processing of multimodal inputs such as text, image, sensor data, etc., and automatic adaptation of different model input formats (e.g., Tensor, JSON, Protobuf) through the Typed Data Channel (TDC).
For example, if you suddenly say, "Explain it in simpler words," the MCP will immediately tell the AI, "Users are white, don't use jargon"
3)Handling complex tasks [auxiliary]
Aids in automating development projects, automated modeling, automated shopping, automated ticket buying and other scenarios.
For example, if you let AI book a flight for you, you need to remember the place of departure, the time of day, the airline preference, and do it step by step.
Why MCP is needed
With the growing popularity of LLM, a standardized model context protocol becomes increasingly important. Different developers and organizations may develop different approaches to context management, and MCP aims to provide a common set of rules and formats to ensure interoperability between different systems.
Limitations of current model training data::
- While LLMs are trained on large amounts of data, they cannot anticipate the needs of all specific scenarios.
- The goal of the MCP is to enable LLMs to dynamically access the contextual information needed to perform a wider range of tasks.
When dealing with tasks in a specific industry or domain, models need access to expertise and data in that domain.MCP can provide a standardized way to pass this information to models.
Improving the accuracy and relevance of models
- Contextual information helps the model better understand the user's intent and generate more accurate and relevant responses. \
- In a dialog system, the MCP can manage the dialog history to ensure that the model remembers previous interactions and generates coherent responses.
- When processing documents, the MCP can provide relevant document content as context to the model so that the model can perform accurate question and answer or summarization.
Handling more complex application scenarios
As LLMs become more widely used, they need to handle more complex tasks :
- Tasks that require access to multiple data sources (e.g., accessing multiple knowledge bases)
- Tasks that require specific operations to be performed
- Tasks that require interaction with external systems (e.g. the recent Blender MCP combined with Claude dialog to generate 3D models)
MCP can provide a standardized way for models to handle these complex tasks.
At the heart of the MCP is
- Enhancing the functionality and application scope of LLM
- Improve LLM performance and accuracy.
- Promote standardization and interoperability of LLMs.
MCP Protocol Explained
MCP Core Concepts .
Main objectives:
can enable LLMs to more easily and securely connect with the outside world for the kinds of information and capabilities they need.
Core Functions:
- Context management: The MCP protocol aims to standardize and optimize the management and delivery of model contexts, thereby improving the performance and application range of models.
- External data integration: Enables LLMs to securely access and manipulate local and remote data, providing an interface for LLMs applications to connect everything.
- Tool Call: The MCP protocol can provide LLMs with the ability to perform specific operations, such as calling APIs, accessing databases, and so on.
Architecture:
MCP follows a client-server architecture and contains the following core components:
- MCP Hosts (MCP Hosts): The LLM application that initiated the request, e.g., chatbot, AI tool, etc.
- MCP Clients:Within the host program, a connection is maintained to the MCP server.
- MCP Servers: External systems that provide data, tools and services.
20 keywords to understand
| byword | fig. values (ethical, cultural etc) | take | appliance |
|---|---|---|---|
| MCP | Standardized LLM integration with data sources reduces the need for custom integration | AI applications that require access to external data, such as chatbots | AI assistants, chatbots, AI-powered IDEs |
| LLM | Provide natural language processing capabilities to generate and understand text | Text Completion, Q&A, Content Generation Scenarios | Text Completion Tool, Customer Service Chatbot |
| Host | Managing multiple client instances and coordinating client-server interactions | Centralized management of AI applications such as desktop applications | Coordination center for AI desktop applications such as Claude Desktop |
| Client | 1:1 connection to the server, handling protocol negotiation and message routing | Communication bridges between hosts and specific servers | Communication components in the MCP architecture, such as the Claude client |
| Server | Expose specific capabilities such as resources, tools, tips, etc. | Provide data or functionality to LLM, such as file access | Filesystem Server, GitHub Server, etc. |
| Resources | Read-only data that can be accessed by LLM, such as files, documents | Access to local files, databases or cloud storage content | LLM query documents, retrieve database information |
| Tools | LLM callable actions or functions, such as running SQL queries | Need to perform specific tasks such as API calls, data processing | Automated tasks such as financial data analysis, web crawling |
| Prompts | Predefined text that guides LLM behavior or provides context | Customize the LLM response or provide specific instructions | Adapt AI outputs for different use cases, such as customer support prompts |
| Sampling | Host coordinates LLM interactions with multiple servers, aggregating contexts | Gathering information from multiple sources for complex queries | Synthesizing multi-source data to provide comprehensive context for LLM |
| JSON-RPC | Communication protocol for client-server message exchange | Standardized Messaging in Distributed Systems | Message interactions in MCP, such as requests and responses |
| Capabilities | Client- and server-supported declaration functions for feature negotiation | Ensure that both parties in the session support specific features | Negotiation of features in MCP sessions, such as tool support |
| Sessions | Stateful client-server connections that allow continuous interaction | Long-term tasks requiring memorization of previous interactions | Conversational AI, continuous multi-step tasks |
| Notifications | One-way messages for events or updates, such as resource changes | Keep the client side informed of server side updates in real time | Real-time status updates in AI applications, such as file modifications |
| Security | Ensure control and security mechanisms for data access and manipulation | Protect sensitive data from unauthorized access | Access control, data encryption in MCP implementation |
| Authorization | Validate and grant access to resources or operations | Ensure that only authorized users or processes can perform operations | User authentication, role access control in MCP server |
| Context | Data provided to the LLM to inform it of the response | LLM interactions that require additional information to provide accurate outputs | Personalized response, application of domain-specific knowledge |
| Stateful | Holding state in a session to preserve context | Need to remember previous exchanges or data interactions | Conversational AI, multi-step processes such as task tracking |
| Protocol Negotiation | Clients and servers initially agree on functions and capabilities | Establishing sessions with known capabilities | Ensure compatibility and functionality at the start of the interaction |
| Message Types | Three message types in the protocol: request, response, and notification | Distinguish between different kinds of communication in a protocol | Message management and routing in MCP implementations |
| Design Principles | Principles guiding MCP design, e.g., easy-to-build servers, high combinability | Rationale for Understanding MCP Architecture and Functionality | Used by developers and architects when designing or extending MCP systems |
MCP 5 main areas
In the following, we will take a closer look at the Model Context Protocol (MCP) protocol in terms of message format, lifecycle, transport protocol, versioning, and additional tool classes.
message format
The MCP protocol usually uses a structured data format for messaging, such as JSON, which has good readability and is easy to parse, making it ideal for exchanging data between different systems.
JSON-RPC 2.0It is a message format that is commonly used by the MCP protocol. It defines a uniform request and response format that allows for more standardized communication between different systems.
life cycle
The life cycle of the MCP protocol involves the processes of connection establishment, data exchange and connection closure between the client and the server.
The life cycle consists of the following stages :
transportation protocol
The MCP protocol can use a variety of transport protocols, commonly including:
- WebSocket: Provides two-way, real-time communication capabilities, suitable for scenarios that require real-time data exchange.
- HTTP/HTTPS: For simple request-response patterns, but relatively poor real-time performance.
releases
Versioning of the MCP protocol is important to ensure compatibility between different versions of the client and server.
Versioning typically includes the following aspects:
- Version Number: Assigns a unique version number to each version of the protocol.
- Compatibility: Clearly define the compatibility rules between different versions, e.g. forward compatible, backward compatible, etc.
- Version Negotiation: Version negotiation is performed between the client and the server when the connection is established to determine the version of the protocol to use.
Additional Tool Classes
In order to simplify the development and use of the MCP protocol, usually provide some additional tools. How to use MCP Different Client support for MCP capabilities are not the same, choose the appropriate Client for your own scenario can be, the following is the MCP official Client collection of each performance:
Why use MCP instead of traditional API ?
| characterization | Traditional APIs | MCP (Model Context Protocol) |
|---|---|---|
| design goal | Provide functional data access | Provide contextual and external data integration for LLMs |
| application scenario | General software applications, web service integration, mobile application development, etc. | Large-scale language modeling (LLM) applications, dialog systems, knowledge graph applications, etc. |
| interaction method | Request-Response | Context-aware, dynamic interaction |
| core functionality | Predefined functions, data acquisition, function calls, etc. | Context management, external data integration, tool calls |
| primary user | software developer | AI model (LLM) |
| context-sensitive | Often not contextually aware | Context-aware, able to understand dialog history, user intent, etc. |
| dynamism | Functions and data structures are predefined, static | Allows LLM to dynamically access external data and tools, dynamically |
| intellectualize | Functional, independent | Intelligent, able to interact more intelligently with the outside world |
| topicality | Real-time is average, depending on the design of the API | Real-time two-way communication can be supported, e.g. using WebSockets |
| Difficulty of integration | Separate integration for each API | One standardized integration capable of connecting multiple services |
| Safety and Control | Each API is defined separately | All tools are standardized and can be unified for security and privilege control |
| scalability | Additional development required | Plug and play, with new features easily added as the AI ecosystem evolves |
Current market MCP applications:
Blender MCP - 3D Modeling
Implementation: using the open source project blender-mcp.
Supabase MCP - Data and Database Integration
Automate the creation and management of databases by connecting Supabase databases to Cursor via MCP.
Figma MCP - Conversion of Figma Designs to Code
MCP provides a unified and standardized way to integrate AI agents and models with external data and tools. Far more than just a new API, it is a powerful connectivity framework that enables intelligent, dynamic and contextual AI applications.
🔗 Resources:
MCP Official Site: https://modelcontextprotocol.io/introduction
MCP content specification: https://modelcontextprotocol.io/specification/2025-03-26
MCP Servers Collection: https://github.com/modelcontextprotocol/servers
MCP Client application collection: https://modelcontextprotocol.io/clients
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