Dear Clients and Partners,
At present, AI has naturally been embedded into our daily routines and work environments. It's no longer surprising to get AI's instant response to our questions such as, "How's the weather today?", "Can you analyze this text?", or "Please organize my schedule."
But have you ever wondered—
How does AI understand our questions and connect to various systems so smoothly and promptly?
This is where MCP (Model Context Protocol) plays a critical role.
MCP is a standardized bridge that connects AI with external systems.
In the past, each system needed to be individually integrated through customized APIs. MCP, however, greatly simplifies and streamlines this process, enabling anyone to easily connect AI to different systems.
In this newsletter, we’ll explore what MCP is, how it works, and where it’s actually being used. Let’s dive in together! ๐
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What is MCP (Model Context Protocol)? |
1. What exactly is MCP?
For AI to become truly intelligent, it needs to go beyond just responding to text—it must be able to use real-time external data. Traditionally, AI models required a separate, custom API integration for each external source (e.g., APIs, databases), which was tedious and inefficient. MCP (Model Context Protocol) solves this challenge. MCP is an open interface protocol that connects AI models to external data sources—like web APIs, databases, and file systems—using a standardized format.
Instead of designing separate integrations for each system, MCP allows all external resources to be accessed through a unified specification. Think of it like the USB-C port for AI—if it supports MCP, it can flexibly plug into a wide range of data.
Also, most LLM APIs today only support Q&A-style interactions, limiting real-time collaboration with external tools or systems. MCP provides a shared language (interface standard) that enables AI to interact with tools, models, and data in real-time.
This makes it much easier for developers to implement integrations and allows users to enjoy faster and more accurate AI experiences. AI is evolving beyond just “talking” to actively thinking, retrieving, and responding with the information needed right now.
2. Why is MCP needed?
At present, most AI systems are locked into specific datasets or functions. For example, while AI can search the web effectively, it often struggles to use internal company documents or specialized tools. AI models traditionally relied on pre-trained, static data and had limited access to real-time information like weather, inventory, or user behavior. If an AI chatbot wanted to provide weather updates, developers had to directly connect it to the weather API, handle authentication, and parse responses.
As services expanded, integration complexity grew—and so did the maintenance burden.
MCP solves this problem.
With MCP, you can access weather, news, document summarization, and more—through a unified interface. No matter how many functions are added, the structure remains simple, enabling AI to expand quickly and flexibly.
3. How does MCP work?
Most current LLM APIs are designed to answer questions, but they struggle to collaborate with external systems or tools. MCP acts as a common language—an interface standard—that allows AI to communicate with tools, data, and external models. Its primary goal is to maintain consistent context exchange between applications and models. It generally works like a client-server system: - First, the AI (client) asks the server, “Hello! What can you do?” This initial connection informs the server of its capabilities.
- Next, the AI makes a request, e.g., “Tell me the weather in Seoul today.” The server finds and sends back the relevant data—this is data retrieval.
- Finally, the AI uses the received information to respond to the user or perform subsequent tasks—such as showing the weather or analyzing the data. This is the response handling phase.
The user sends a request through various applications (hosts). The MCP Client converts the request and sends it to multiple functions or an MCP Server. Just as USB connects different devices with a single standardized interface, MCP connects AI system components in a consistent structure. The MCP Client serves as an intermediary between the host and server. It organizes the user’s request, adds contextual information, and packages it into a structured message that the AI can understand—then sends it to the MCP Server. It also returns the AI's response back to the Host. Thanks to MCP, developers can now integrate complex APIs much more easily, and users benefit from more accurate and real-time AI responses.
AI is no longer just a machine to provied answers to questions —it’s evolving into an intelligent agent that actively communicates with the external world.
4. How is MCP different from traditional API approaches?
Traditional API implementations require custom call methods and prompt structures for each tool or service. Developers had to write specific code for each one, making system integration complex and maintenance difficult. In contrast, MCP standardizes various feature requests into a unified structure, enabling the model to receive instructions in a consistent manner. This streamlines prompt design, tool integration, and role assignment, all within a standardized framework. |
MCP can be widely applied across various industries. It simplifies AI-system integration and brings real-world benefits in fields like :
โช๏ธ Smart Public Services
AI collects and analyzes public data (e.g., traffic, environment, population) in real-time to generate policy reports and integrate insights across various activities of departments such as _Traffic management, energy efficiency, public safety, environmental monitoring.
โช๏ธ Enterprise Workflow Integration
MCP unifies fragmented systems across departments, streamlining data flows and improving operational efficiency.
โช๏ธ Automated Civil Service Responses
When a citizen asks, “How do I apply for a parking space?”, AI uses MCP to connect to public service systems and immediately retrieve accurate, real-time answers.
โช๏ธ Emergency Response Automation
During disasters, AI uses MCP to gather data from communication, transport, and healthcare systems—supporting fast, precise crisis response.
โช๏ธ IoT (Internet of Things)
By standardizing connections across various IoT devices, MCP simplifies integration and enables efficient device management.
โช๏ธ Manufacturing (Smart Factory)
MCP allows for integrated management of production planning, quality control, predictive maintenance, and more—boosting productivity and reducing defects.
โช๏ธ Finance
MCP unifies AI systems for transaction monitoring, risk analysis, customer service, and fraud detection, enabling accurate, consistent decision-making.
โช๏ธ Healthcare
From diagnosis support and medical imaging analysis to patient monitoring and drug development, MCP helps consolidate diverse medical AI systems into a unified platform for precise diagnoses and efficient treatment.
โช๏ธ Autonomous Driving
MCP helps integrate AI modules for perception, decision-making, and control—enabling stable, reliable self-driving environments.
Spacebank possesses the next-generation robot control platform RoboViewX, which monitors both robot behavior and operation while providing an intuitive and immersive user experience.
RoboViewX is a heterogeneous robot integrated control solution that visually monitors robots through a real-time 3D viewer and video feed. It allows users to observe robot movements and status in real time within a web environment, and enables intuitive monitoring via various charts and dashboards.
Notably, by leveraging MCP (Mission Control Platform), RoboViewX seamlessly integrates robot data with external systems, making real-time monitoring possible even in cloud environments. Additionally, the platform supports real-time 3D control, AI-based predictive maintenance, spatial awareness and multi-camera integration, and precise robot condition diagnostics.
Through RoboViewX, Spacebank empowers users to operate robots in a more intuitive and immersive environment, and supports the development of intelligent automation systems tailored to various industries. |
๐Spacebank Stresses Building a Software-Based Robot Ecosystem๐
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On 20 June 2025, Spacebank participated in the Kickoff Workshop for the Next-Generation Shared Robot Platform Development Project.
This workshop marked a significant milestone in a multi-year national R&D initiative aimed at developing a next-generation SDR (Software-Defined Robot) platform. The outcomes of the first-year research phase were reviewed and the plans for the second year were discussed at the workshop.
The workshop was attended by key organizations leading the project, such as KIST (Korea Institute of Science and Technology) and KETI (Korea Electronics Technology Institute), along with all the industry and academic stakeholders including Spacebank, Goorm Networks, Robomation, Yujin Robot, Rovros, CDR System, LAS Tech, KIRO (Korea Institute of Robot and Convergence), Hanyang University ERICA, and Pusan National University.
During the event, Mrs. Woney Lee, CEO of Spacebank, gave a presentation highlighting the company’s role in developing a cloud-based SDR common service framework and unified interface system, which was selected last year as an integrated innovative product under the national industrial technology development program for the machinery, robotics, and equipment sector.
In her remarks, Msr. Lee emphasized:
“This second-year kickoff marks an important step towards positioning the SDR platform—designed for high availability, scalability, and cost-efficiency—as a leader in Korea’s robotics industry.”
“We will continue to strengthen collaboration with related institutions and build a foundation that allows robot services to be deployed more easily and quickly through SDR technology.”
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๐AIoT Wright Installed at Incheon City Second Municipal Geriatric Hospital๐
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The AIoT Wright (HumanCare) solution—registered as an Innovative Product (No. 25496304) by the Public Procurement Service of Korea—has been successfully installed at Incheon City Second Municipal Geriatric Hospital, operated by Inseong Medical Foundation.
Powered by AI-driven smart radar sensors, AIoT Wright monitors elderly patients in a non-contact manner by detecting their movements. The system provides real-time monitoring of potential hazards such as falls or prolonged immobility, helping ensure patient safety around the clock.
Trusted for Both Technology and Quality
Spacebank is rapidly emerging as a leader in the AI and smart healthcare market, backed by robust technological and quality credentials.
Key achievements include:
• 10 patents, 13 trademarks, 1 design right, and 7 copyrights
• Registered as an Innovative Product with the Public Procurement Service
• Certified with GS Grade 1, CSAP, and NIPA Quality & Security Certifications
These milestones demonstrate both technical credibility and market scalability.
Expanding Smart Safety Across Healthcare and Public Institutions
Going forward, Spacebank aims to expand its reach by delivering intelligent safety management solutions to more public institutions and healthcare facilities.
By combining AI and IoT technologies, Spacebank continues its mission to protect lives and enhance care environments through smart, non-intrusive monitoring systems tailored for the needs of vulnerable clients.
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Spacebank.Korea raiid.ai@spacebank.co.kr
46, Dallaenae-ro, Sujeong-gu, Seongnam-si, |
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Dear Clients and Partners,
At present, AI has naturally been embedded into our daily routines and work environments. It's no longer surprising to get AI's instant response to our questions such as, "How's the weather today?", "Can you analyze this text?", or "Please organize my schedule."
But have you ever wondered—
How does AI understand our questions and connect to various systems so smoothly and promptly?
This is where MCP (Model Context Protocol) plays a critical role.
MCP is a standardized bridge that connects AI with external systems.
In the past, each system needed to be individually integrated through customized APIs. MCP, however, greatly simplifies and streamlines this process, enabling anyone to easily connect AI to different systems.
In this newsletter, we’ll explore what MCP is, how it works, and where it’s actually being used. Let’s dive in together! ๐
1. What exactly is MCP?
Instead of designing separate integrations for each system, MCP allows all external resources to be accessed through a unified specification. Think of it like the USB-C port for AI—if it supports MCP, it can flexibly plug into a wide range of data.
Also, most LLM APIs today only support Q&A-style interactions, limiting real-time collaboration with external tools or systems. MCP provides a shared language (interface standard) that enables AI to interact with tools, models, and data in real-time.
This makes it much easier for developers to implement integrations and allows users to enjoy faster and more accurate AI experiences. AI is evolving beyond just “talking” to actively thinking, retrieving, and responding with the information needed right now.
2. Why is MCP needed?
At present, most AI systems are locked into specific datasets or functions. For example, while AI can search the web effectively, it often struggles to use internal company documents or specialized tools.
As services expanded, integration complexity grew—and so did the maintenance burden.
MCP solves this problem.
With MCP, you can access weather, news, document summarization, and more—through a unified interface. No matter how many functions are added, the structure remains simple, enabling AI to expand quickly and flexibly.
3. How does MCP work?
MCP acts as a common language—an interface standard—that allows AI to communicate with tools, data, and external models.
Its primary goal is to maintain consistent context exchange between applications and models.
It generally works like a client-server system:
The user sends a request through various applications (hosts).
The MCP Client converts the request and sends it to multiple functions or an MCP Server.
Just as USB connects different devices with a single standardized interface, MCP connects AI system components in a consistent structure.
The MCP Client serves as an intermediary between the host and server.
It organizes the user’s request, adds contextual information, and packages it into a structured message that the AI can understand—then sends it to the MCP Server. It also returns the AI's response back to the Host.
AI is no longer just a machine to provied answers to questions —it’s evolving into an intelligent agent that actively communicates with the external world.
AI collects and analyzes public data (e.g., traffic, environment, population) in real-time to generate policy reports and integrate insights across various activities of departments such as _Traffic management, energy efficiency, public safety, environmental monitoring.
MCP unifies fragmented systems across departments, streamlining data flows and improving operational efficiency.
When a citizen asks, “How do I apply for a parking space?”, AI uses MCP to connect to public service systems and immediately retrieve accurate, real-time answers.
During disasters, AI uses MCP to gather data from communication, transport, and healthcare systems—supporting fast, precise crisis response.
By standardizing connections across various IoT devices, MCP simplifies integration and enables efficient device management.
MCP allows for integrated management of production planning, quality control, predictive maintenance, and more—boosting productivity and reducing defects.
MCP unifies AI systems for transaction monitoring, risk analysis, customer service, and fraud detection, enabling accurate, consistent decision-making.
From diagnosis support and medical imaging analysis to patient monitoring and drug development, MCP helps consolidate diverse medical AI systems into a unified platform for precise diagnoses and efficient treatment.
MCP helps integrate AI modules for perception, decision-making, and control—enabling stable, reliable self-driving environments.
๐Spacebank Stresses Building a Software-Based Robot Ecosystem๐
๐AIoT Wright Installed at Incheon City Second Municipal Geriatric Hospital๐
Powered by AI-driven smart radar sensors, AIoT Wright monitors elderly patients in a non-contact manner by detecting their movements. The system provides real-time monitoring of potential hazards such as falls or prolonged immobility, helping ensure patient safety around the clock.
Trusted for Both Technology and Quality
Spacebank is rapidly emerging as a leader in the AI and smart healthcare market, backed by robust technological and quality credentials.
Key achievements include:
• 10 patents, 13 trademarks, 1 design right, and 7 copyrights
• Registered as an Innovative Product with the Public Procurement Service
• Certified with GS Grade 1, CSAP, and NIPA Quality & Security Certifications
These milestones demonstrate both technical credibility and market scalability.
Expanding Smart Safety Across Healthcare and Public Institutions
Going forward, Spacebank aims to expand its reach by delivering intelligent safety management solutions to more public institutions and healthcare facilities.
By combining AI and IoT technologies, Spacebank continues its mission to protect lives and enhance care environments through smart, non-intrusive monitoring systems tailored for the needs of vulnerable clients.
https://www.spacebank.company/
46, Dallaenae-ro, Sujeong-gu, Seongnam-si,