Uncovering the Model Context Protocol (MCP): A Primer for the Next Generation of Engineers

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MCP (Model Context Protocol) is an open standard protocol that enables large language models (LLMs) to communicate seamlessly with external tools and data sources, greatly expanding the capabilities of AI.Core Goals：Establish a standardized protocol similar to USB-C, unify the interactive interface between AI models and external resources, and achieve " integrate once, run everywhere ". To solve the problem that current AI models cannot fully realize their potential due to data island limitations, MCP enables AI applications to securely access and operate local and remote data, providing an interface for AI applications to connect everything.

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1. Model Context Protocol (MCP) Background

MCP (Model Context Protocol) is an open protocol launched by the artificial intelligence company Anthropic (open source Claude) in November 2024, which aims to solve the communication standardization problem between LLM and external data sources. Its background can be summarized as follows:

• Data silo limitations : Traditional AI models are limited in their potential because they cannot efficiently access dispersed local or remote data (such as databases, APIs, and file systems).
• Integration fragmentation : Different data sources require customized development interfaces, which increases development costs and makes expansion difficult (the overall code level of LangChain and LlamaIndex is too high).
• AI ecosystem needs : With the expansion of LLM application scenarios (such as Claude, Qwen, etc.), there is an urgent need to unify protocol connection tools, content libraries and business systems.

The proposal of MCP is similar to creating a "USB-C port" for AI applications to achieve a universal connection standard.

2. Why do we need MCP? What pain points does it solve?

Current pain points

1. Data isolation : LLM cannot directly access internal and external data sources (such as databases and APIs) of the enterprise, requiring complex customized development.
2. Security risks : Traditional integration methods may expose sensitive data and lack standardized authorization mechanisms.
3. Tool fragmentation : Different data sources need to be adapted independently, resulting in high maintenance costs.
4. Contextual limitations : The model relies on static pre-training data and is difficult to dynamically combine with real-time business data to generate responses.

MCP Solutions

• Unified interface : Connect multiple data sources (files, APIs, databases, etc.) through standardized protocols, simplify integration, and provide a universal bridge.
• Service providers : They can open their own APIs and functions based on MCP and integrate into the larger ecosystem.
• Developers : No need to build integration logic from scratch, you can directly use the existing MCP services to enhance the capabilities of AI Agents.
• Security control : User authorization mechanism ensures data operation compliance and supports local deployment to avoid data leakage.
• Dynamic context : Real-time acquisition of external data enhances the quality of model responses, such as combining database queries to generate accurate answers.

Solve the problem of reinventing the wheel

The standardized design of MCP allows developers to reuse resources contributed by the community. For example, once an MCP server that supports GitHub operations is developed, other developers can call it directly without having to implement it repeatedly. This "develop once, use multiple times" model greatly improves efficiency.

3. Basic Concepts of MCP

• Definition : The MCP protocol aims to solve the fragmentation problem when traditional AI systems are integrated with external data sources and tools, and provide a standardized interface for the interaction between AI models and the external world.

• Function : It is like a "universal translator" for AI, allowing AI to safely and controllably access various external resources, such as file systems, databases, APIs, etc., and perform specific tasks. For example, when you ask AI to organize meeting records in your computer, it can directly access the file system through the MCP protocol to complete operations such as classification and archiving, and generate summaries.

The role of MCP is succinctly explained through two metaphors:

1. Universal remote control : MCP is like a universal remote control that can control all home appliances, allowing AI assistants to operate different enterprise systems with unified commands, without the need to equip each system with a specific control method.

• It can not only allow the "TV" to play surveillance videos (call the security system), but also allow the "air conditioner" to adjust the data temperature (adjust the server load), and also allow the "sound system" to broadcast business reports (trigger speech synthesis)

1. USB-C interface for AI : MCP has also been compared to the USB-C interface in the field of AI, providing a unified standard that allows AI to easily connect to various peripherals (such as databases, mail systems, and report generation tools), avoiding the need for multiple dedicated interfaces and simplifying the AI ​​integration process.

• Through MCP, an AI assistant can connect to a USB flash drive (database), plug in a printer (mail system), and connect to a monitor (report generation). The interfaces are the same, but the functions are different.

1. AI universal translator : Allows AI to safely and controllably access various external resources, such as file systems, databases, APIs, etc., and perform specific tasks. For example, when you ask AI to organize meeting records in your computer, it can directly access the file system through the MCP protocol to complete operations such as classification and archiving, and generate summaries.

4. MCP Core Components

MCP has five core components:

1. MCP Host : Applications that run AI models, such as Pycharm, Visual Studio, Claude Desktop, Cursor, Cline.
2. MCP Client : It is used to maintain a 1:1 connection with the Server in the Hosts application. Multiple MCP clients can be run in a host application to connect to multiple different servers at the same time.
3. MCP Server : The core part, providing resources (Resources), tools (Tools), and prompts (Prompts) to Clients through standardized protocols.
4. Local resources : directly accessible data such as local files and databases.
5. Remote resources : External APIs or cloud services, such as GitHub or Slack.

• MCP Server capabilities :
• Resources : static data sources (such as file contents, API responses).
• Tools : callable functions or services (such as SQL queries, third-party APIs).
• Prompts : predefined task templates that guide the model to generate responses in a specific format.
• Communication mechanism :
• stdio transmission : MCP defines a message communication protocol based on JSON-RPC 2.0. Local processes exchange JSON-RPC messages through standard input and output.
• HTTP with SSE : Remote communication uses Server-Sent Events.
• Security and Control :
• Message transparency : Pure JSON format is used to encapsulate three message types: request (with unique ID), response (including result/error) and notification (no reply). Each message contains the method name and parameters, similar to function call, intuitively expressing behaviors such as "perform operation/get data".
• Developer-friendly : Compared with binary protocols (such as gRPC), JSON messages can be read manually and are easier to debug with structured logs. The protocol layer automatically handles request-response matching, error transmission, and concurrency management, so developers only need to focus on business logic.
• Users need to explicitly authorize tool calls, and operation records are auditable.

5. MCP Workflow

Working principle : The MCP protocol adopts a client-server architecture, and implements dynamic context delivery and flexible tool invocation through standardized interfaces (such as JSON-RPC-based communication). When AI needs to access external data or tools, the MCP client sends a request to the corresponding MCP server, which processes and returns the result, thus enabling AI to interact with external resources.

Taking PostgreSQL database query as an example, the typical process is as follows:

1. Initialize the connection : The Client starts the Server process and obtains the capability list (Tools/Resources/Prompts).
2. User query : User asks a question (such as “Query the order with the highest amount”).
3. LLM decision : Model judgment requires calling tools (such as SQL queries), and the Client sends a request to the Server.
4. Execution and return : The server executes the tool (generates SQL and queries the database), and the results are returned to the LLM to generate the final response.
5. User display : The response is displayed after authorization, and supports viewing the original data source (such as SQL statements).

6. Differences between MCP and Function Calling

7. MCP Server Classification and Application

MCP Server is the core of MCP service, which mainly segments the applications with high popularity from three perspectives: official, third-party and community MCP Server.

Official MCP Server

Third-party MCP Server

Community MCP Server

8. Best Practices of MCP in Web Scraping Scenarios

Assume that dynamic web page data crawling needs to be implemented through MCP:

1. Building a crawler :

• definition WebCrawlerTool, the parameters include the URL and parsing rules (such as XPath).

• Register as MCP Server :
• Deploy the tool as a server and expose call_tool Method to execute crawling logic.
• Dynamic call :
• LLM generates tool call requests based on user questions (such as "get the headlines of a news website") and returns structured data.
• Security Enhancements :
• Limit crawling frequency and control sensitive operations through user authorization.

Standard development process

1. Define functions : Clarify what functions the MCP server will provide, such as file management, database query, network services, etc.
2. Implement the MCP layer : Follow the standardized MCP protocol specifications for development, including message format, communication logic, etc.
3. Select a transport : Choose between a local transport (like stdio) or a remote transport (like Server-Sent Events/WebSockets).
4. Create resources/tools : Develop or connect to specific data sources and services that the MCP will interact with, such as file systems, databases, APIs, etc.
5. Set up the client : Establish a secure and stable connection channel between the MCP server and the client.

IX. Application Scenarios

• Trip Planning Assistant : The AI ​​assistant seamlessly checks calendar availability, books flights, sends confirmation emails, etc. through the MCP server, without the need to develop integration code for each tool separately.
• Advanced IDE (Intelligent Code Editor) : The development environment connects the file system, version control, package manager, and document system through the MCP protocol to achieve richer context-aware capabilities and smarter code suggestions.
• Complex data analysis : The AI ​​analytics platform automatically discovers and interacts with multiple databases, visualization tools, and simulation systems through a unified MCP layer, eliminating the need to manually manage connections.
• File management : Let the AI ​​assistant organize files, archive them, generate summaries, etc., such as accessing the local file system through the MCP server.
• Information query : Directly search local documents or query PDF content to obtain the required information.
• Communication assistance : Draft a Slack message based on a report, or summarize a team chat.
• Network services : Get weather, map navigation or news briefings through the MCP server.

10. Summary and Outlook

Summarize:

• Value : Unified the interaction standards between LLM and external systems, reduced integration costs, and improved model practicality.
• Limitations : Complex logic (such as loops) is not currently supported, and remote connections rely on local transfers.

Outlook:

• Remote connections : The 2025 roadmap plans to support OAuth 2.0 authentication and stateless services.
• Ecosystem expansion : More companies and communities join (such as Block and Apollo) to promote tool standardization.
• Industry applications : Combine private data to enhance model professional capabilities in fields such as medicine and finance.