AI Automation key – What is the MCP new standard from Claude and MS?

● Claude MCP: The AI Revolution is HERE!

MCP Protocol: Everything About Standardized Solutions Innovating AI Agents

1. Basic Concepts and Key Points of MCP

MCP is a protocol that provides various tools to AI agents in a standardized way.
Thanks to this protocol, functions can be used in the same way across multiple platforms, reducing redundant coding and development time.
In other words, it plays a role in connecting tools simply like a “USB port,” replacing the existing complex function definitions.
In particular, in the face of rapid changes in the global economy and the IT industry, MCP standardization can be seen as an innovative technology comparable to investment strategies and economic outlook analysis.

2. Necessity of Standardization Before and After AI Agent Construction

∙ Existing: Tools and functions were developed individually for each AI agent, resulting in low reusability.
∙ Problem: The same functions had to be re-implemented each time various platforms or frameworks (pantic AI, n8n, Cursor, etc.) were used.
∙ After MCP introduction: Tools are modularized into standardized packages, making them usable in different environments in the same way.
∙ Conclusion: With MCP, developers can efficiently implement AI agents without redundant work and easily add functions such as economic indicator analysis or market trend tracking.

3. How to Build MCP Server and Client

∙ Building MCP Server
– Various examples and SDKs are available in the official GitHub repo.
– For example, in the case of the Python version, the server can be easily run using uvicorn, etc.
– The MCP server provides various functions such as file system, Google Drive, and web scraping, allowing AI agents to handle complex tasks.
∙ Integration with n8n and Other Platforms
– By installing the MCP community node in workflow tools such as n8n, you can easily integrate with various MCP servers.
– Connect desired services and tools via JSON format configuration files, and use them like API endpoints.
∙ Developing MCP Client
– You can implement the client directly using the Python SDK.
– The client receives the list of tools provided by the MCP server and delivers those functions to the AI agent, ensuring easier integration and reusability.

4. Various Use Cases and Future Prospects of MCP

∙ Case Analysis
– Previously, separate tool development was required for each service, but after the introduction of MCP, complex functions such as web search, file management, and database integration can be implemented efficiently through tool standardization.
– For example, the MCP server linked with CLA Desktop, Cursor, etc. actually automates various tasks such as file system, web crawling, and screenshots.
∙ Future Development of MCP
– Currently, MCP is not limited to simple tool standardization, but has the potential to expand to authentication, authorization, and even monetization models.
– Depending on future market trends and global economic changes, there is ample room for it to become a core element of technology development and investment strategies.
∙ Comparison with Economic Outlook
– MCP’s standardization innovation presents a new paradigm in AI tool development and utilization, just like new investment strategies in the global economy.
– Although not directly related to economic indicators or market trends, it can be understood in a similar context to the impact of technological development on the overall economy.

Summary

The MCP protocol is a solution that standardizes and provides tools to reduce duplicate code in AI agent development.
Unlike existing distributed tool development, with MCP, file management, web search, data processing, etc. can be performed in the same way on various platforms.
Server and client construction methods can be easily followed by referring to official documents and GitHub examples, and efficient workflows can be implemented by linking with tools such as n8n.
In addition, MCP will expand into authentication, authorization, monetization, etc. in the future, and will provide innovative value similar to important investment strategies and economic outlook analysis technologies in large-scale market changes such as the global economy.
SEO Core Keywords: Global Economy, Economic Outlook, Market Trends, Investment Strategies, Economic Indicators.

*Source : [Cole Medin] Claude MCP has Changed AI Forever – Here’s What You NEED to Know

● Unlock Agentic AI with MCP

Model Context Protocol (MCP) for Understanding the Convergence of AI Models and Economic Technology Trends

1. Concept and Background of MCP

MCP stands for Model Context Protocol.
Here, the model refers to AI, especially Large Language Models (LLM).
Recently, trends in AI technology and economic outlooks have been changing rapidly, and MCP serves as an innovative interface in response to these changes.
Important keywords related to economic outlook, AI, technology, innovation, and trends are naturally embedded.

2. Importance of Context in AI Models

AI models need to perform more complex tasks beyond simple question-and-answer levels.
For example, even in the process of checking the sum of orders from an SQL database and comparing it with a presentation file,
MCP plays a role in providing context so that the model can access each data source.
Using MCP in this way allows simultaneous access to multiple servers and sources to perform a single task.

3. Key Components of MCP (Context Primitives)

There are four primitives that an MCP server can provide to an AI model:
• Tools: Provides functions that the model can directly execute, such as creating and updating databases.
• Resources: Includes materials attached to the model, such as presentation files.
• Sampling: A method of calling other models to obtain results, opening up the possibility of collaboration between models.
• Parameterized Prompts: Allows additional information to be provided in the form of templates upon request.
All of these elements enable efficient task processing in various fields, including economic outlooks.

4. Roles of MCP Server and Client

The MCP system is divided into a server and a client.
The server implements Tools and Resources, and
the client calls these to perform the necessary tasks.
For example, a chat interface like Claude desktop acts as a client, and
an MCP server installed internally or in the cloud accesses an SQLite database or files.
This structure can be linked to innovative data processing methods in the economic technology industry.

5. Message Structure and Reflection Function

The MCP protocol has a well-defined message structure transmitted between the client and server.
In particular, through the Reflection function, the client can ask the server what Tools and Resources are available.
Unlike other API standards such as GraphQL or gRPC, this feature allows the AI model to directly explore the context provided to it.
This enables the provision of accurate information necessary for economic analysis, data processing, and more.

6. Transmission Methods: Standard IO and Server Sent Events

MCP supports two transmission methods:
• Standard IO: Runs an MCP server locally and communicates through input/output streams in Unix or Windows.
• Server Sent Events (SSE): A network-based method that communicates with the server via HTTP/HTTPS.
Depending on the use case, an appropriate transmission method can be selected and utilized for real-time processing related to economic data.

7. Practical Examples and Coding Approach

When actually building an MCP server, languages like TypeScript are used to
define the server specifications (name, version) and Tools, Resources.
Descriptions of functions and timing of use are added to each Tool so that the AI model can correctly recognize them.
Execution can be tested through tools like Inspector, making it very efficient to quickly verify before applying it to an actual economic model.

8. Practical Application and Key Implications of MCP

MCP does not replace existing API specifications (GraphQL, gRPC, etc.), but rather
serves as a user interface that helps AI models efficiently receive the context needed for tasks.
• When MCP is used with a backend API, the AI model can perform complex tasks using only high-level abstract commands.
• Rather than putting all functions into a single MCP server, it is more efficient to access multiple servers and distributed systems.
• It is important to increase the level of abstraction of the server to minimize exposure of unnecessary details.
All of this information greatly helps in building efficient data integration and analysis systems in a changing environment, in line with the latest trends related to economic outlooks and AI technology innovation.

Summary

MCP (Model Context Protocol) allows AI models, especially Large Language Models (LLM), to
efficiently acquire the necessary context from external servers.
It consists of four main primitives (tools, resources, sampling, parameterized prompts) and
can access economic data and other attached materials.
MCP clearly distinguishes between client and server, and through the Reflection function,
the tools and materials provided by the server can be checked.
Transmission methods support both Standard IO and SSE, and
the MCP server can be effectively built through practical coding examples.
The key is to use MCP in parallel with the backend API so that
AI models can use high-level abstract commands to perform complex tasks.

[Related Articles…] AI Innovation Trends Summary | Global Economic Outlook Update

*Source : [Jack Herrington] Model Context Protocol (MCP): The Key To Agentic AI

● Crush Developer Email with MCP and AI

Agent-Centric Future: Automation and AI Innovation Performing Instead of Humans

1. Redefining Agent and Developer Experience

Agent-centric applications are emerging.
Database design and email sending methods, which were previously led by humans, are being reorganized by AI and automation agents.
Developers now need to consider the Agent Experience as well as human users.
Products and processes must be innovated to meet new economies, global markets, outlooks, investments, and market trends.

2. Innovation and Simplification of Email Services

Existing email solutions have been delayed due to complex registration and authentication procedures, but the new system for agents is designed to allow quick registration and immediate email sending.
The value of text-based content has been re-examined with the use of LLM (Large Language Model), and a simpler format than HTML is suitable for agents.
Essentially, email products are being restructured based on key keywords such as economy, global, outlook, investment, and market.

3. AI Tools and Agent Building: MCP Protocol and API Integration

MCP (Multi-step Command Protocol) for agents is creating new agent workflows in conjunction with existing APIs.
Developers need to redesign the existing SDKs and APIs into an agent-centric structure that minimizes repetitive tasks.
The agent functions as a ‘tool’ rather than a user, increasing the efficiency of both developers and marketers.

4. Reconsidering Use Cases and Product Development

From the initial stages of product development, complex authentication, permission management, and action records must be redesigned from an agent perspective.
For example, in all email-related services, it is important to reduce friction in the registration, verification, and transmission processes so that agents can take quick actions.
Design strategies that consider both user and agent interaction must keep pace with changes in the global economy and investment market.

5. Future Prospects: Agent-Driven Economic Environment

In the future, most tasks and processes are expected to rely on agents rather than humans.
This era change requires innovation in the core issues related to global, outlook, investment, and market as well as existing economic paradigms.
Products and services need to simplify the experience of both users and agents in line with the rapidly evolving technology environment and utilize real-time data.

Summary

Agent-centric applications and AI tools are replacing existing human-centric services, leading to rapid innovation in areas such as email and API integration.

Developers and marketers must redesign product sign-up, authentication, and action records exclusively for agents to build an efficient service environment that meets global, economic, outlook, investment, and market issues.

Simplifying existing complex procedures and agent workflows using text-based content and MCP protocols are expected to be key competitive advantages in the future.

[Related Posts…]

Global Innovation Changed by Agents

Artificial Intelligence Era, Economic Outlook and Investment Strategy

*Source : [a16z] Automating Developer Email with MCP and AI Agents