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Playing with Agent MCP protocol

Written by
Caleb Hayes
Updated on:July-13th-2025
Recommendation

Explore how the MCP protocol launched by Anthropic revolutionizes the way AI interacts with data sources.

Core content:
1. The MCP protocol solves the problem of data silos and reduces development costs
2. Enhances security and improves development efficiency
3. Improves flexibility, scalability and user experience, and promotes AI ecological standardization

Yang Fangxian
Founder of 53AI/Most Valuable Expert of Tencent Cloud (TVP)

Playing with Agent MCP protocol

MCP (Model Context Protocol) is an open standard protocol launched by Anthropic in November 2024. It aims to provide a standardized way for large language models (LLMs) to interact with external data sources, tools and services.

Why design the MCP protocol?

The emergence of the MCP protocol (Model Context Protocol) is mainly to solve the complexity, security and scalability issues of integration between AI systems and external data sources and tools. The following are the main necessities and problems solved by the MCP protocol:

1. Solve the data island problem

  • In the traditional method, AI models need to develop connectors for each data source separately, resulting in high development costs and cumbersome maintenance.

  • The MCP protocol provides a unified, standardized interface that allows AI systems to seamlessly connect to a variety of data sources and tools without the need for separate development for each data source.

2. ⚡ Improve development efficiency

  • Using the MCP protocol, developers can connect to multiple data sources through one-time development without having to rewrite code.

  • This standardized integration approach greatly reduces development and maintenance costs.

3. Enhanced security

  • The MCP protocol reduces the risk of AI models performing malicious operations or accessing unauthorized data by defining well-defined interfaces and access control mechanisms.

  • It also ensures the security of data transmission and protects user privacy through user authorization mechanism.

4. Improve flexibility and scalability

  • The MCP protocol supports plug-in extensions, and developers can add new functions and services at any time according to business needs.

  • It also supports cross-platform interoperability, allowing different AI tools to share the same set of connection methods.

5. Improve user experience

  • The MCP protocol enables AI models to better understand the context of complex tasks and multi-round conversations through context management and state tracking mechanisms.

  • This mechanism is particularly important in scenarios such as project management, customer service, and software development, and can significantly improve the work efficiency and accuracy of the model.

6. Promote standardization of the AI ​​ecosystem

  • As an open standard, the MCP protocol aims to become a standard bridge connecting AI systems with external data resources.

  • Its promotion will help form an interconnected AI ecosystem and promote the widespread application of AI technology.

When you design an Agent?

When you are ready to develop a stock selection agent, the difference between using the traditional API and using the MCP protocol is obvious;

Features/Scenarios
Using the Legacy API
Using MCP protocol
Development Complexity
Integration code needs to be written separately for each data source (such as stock quotes API, financial data API, news API, etc.), which makes development complicated.
Through one integration of the MCP protocol, multiple data sources can be connected without the need for separate development for each data source.
Data source connection
Connections to each data source need to be managed manually, including authentication, context passing, and error handling.
Dynamically discover and connect multiple data sources without having to pre-define fixed code for each integration.
Real-time
Data updates require manual polling or implementation through Webhooks, and real-time performance is poor.
It supports real-time two-way communication and real-time data update, which is suitable for real-time stock selection.
Security
Security policies need to be configured separately for each data source, which is prone to vulnerabilities.
Built-in standardized security and permission controls to reduce the risk of data leakage.
Scalability
Every time a new data source or tool is added, the integration code needs to be redeveloped and maintained.
Functionality can be expanded simply by connecting new MCP servers.
Context Management
Lack of unified context management makes it difficult to integrate multi-source data.
Provides unified context management to facilitate integration of multi-source data for comprehensive analysis.
Application Scenario
Suitable for scenarios with extremely high performance requirements and fixed data sources.
Suitable for stock selection scenarios that require dynamic interaction, multi-source data integration and context awareness.

How to design MCP?

Core Concepts

  • Standardized connectivity : MCP is like the “USB-C port” for AI, allowing AI models to connect to different data sources and tools in a unified way.

  • Simplified integration : Compared with traditional APIs, MCP reduces the need to write code separately for each data source or tool, reducing development complexity.

  • Dynamic discovery and two-way communication : MCP supports dynamic discovery of external tools and services and enables two-way communication between AI models and these tools.

Architecture and components

  • MCP Hosts : Carriers of AI applications, such as chatbots or AI-driven IDEs.

  • MCP Clients : Reside inside the host and establish a one-to-one connection with the MCP server.

  • MCP Servers : Lightweight applications that expose specific functionality and connect to data sources or APIs.

  • Resources : Data and content exposed by the server that can be read by clients and used as context for LLM interactions.

  • Tools : Functions provided by the server that can be called by LLM.

Let’s take a look at the servers in the MCP ecosystem.

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