22.04.2025

What is Model Context Protocol (MCP)? Standard Connection Protocol for AI

Discover how the Model Context Protocol (MCP) empowers AI models to go beyond static training data by enabling standardized access to real-time tools and external data sources—transforming them into truly context-aware, action-capable systems.

Today, artificial intelligence (AI) technologies are playing an increasingly critical role across numerous areas of our lives. In particular, the remarkable advancements in large language models (LLMs) in recent years have significantly enhanced their capabilities to perform complex tasks and generate human-like text. However, even the most advanced LLMs are typically limited to their training data and may face challenges accessing real-time or proprietary information. This limitation can hinder the full realization of AI applications’ potential. It is precisely at this point that the concept of the Model Context Protocol (MCP) comes into play.

This report aims to provide a comprehensive overview of what MCP is, where it is used, its technical architecture, benefits, and limitations—in a way that is understandable for both technical experts and general business audiences. The evolution of AI has progressed from isolated capabilities to systems that can interact with real-world data and tools in a more context-aware manner. MCP represents a pivotal step in this evolution. Early LLMs were restricted by the information embedded in their training datasets, limiting their ability to access live or custom data. As users began to demand more relevant and personalized responses, this limitation became more pronounced. MCP addresses this challenge by enabling models to connect to external sources in a standardized way—empowering AI not just to deliver information, but also to perform real-world actions.

What is MCP and Why Was It Developed?

The Model Context Protocol (MCP) is an open standard introduced by Anthropic in late 2024. Its primary purpose is to enable AI models to universally connect with external data sources, a wide array of tools, and various operating environments. Think of this protocol as a USB-C port for AI applications. Just as USB-C enables different devices to connect with peripherals and accessories through a single standard, MCP allows AI models to interface with multiple data sources and tools through a unified protocol.

One of the fundamental motivations behind the development of MCP is to overcome the issue of data isolation in AI models. These models often remain confined within information silos and outdated systems. When access to a new data source is required, a custom integration must typically be built for each source—making truly connected systems difficult to scale and expensive to maintain. MCP addresses this by replacing fragmented integrations with a single universal protocol.

Moreover, for LLMs to provide reliable, personalized, and useful outputs, direct access to customer data is essential. MCP standardizes this access structure. In the current landscape, connections between different AI models and external systems typically rely on custom solutions. This increases development costs and hinders seamless interoperability. MCP transforms this “M×N problem”—where every AI application must be manually integrated with every tool—into a more manageable “M+N problem,” where applications and tools communicate through a single standard.

For instance, when multiple AI applications (e.g., a chatbot or a coding assistant) need to interact with multiple external systems (e.g., GitHub, Slack, or a database), each combination requires a separate integration. MCP simplifies this complexity by allowing all AI apps and external systems to speak a common “language.” This eliminates the need to rebuild integrations every time a new system or tool is introduced.

MCP’s Core Components and Technical Architecture

At its foundation, the Model Context Protocol (MCP) is based on a client-server architecture, where a main host process connects to multiple servers. Communication between clients and servers is handled via a messaging protocol called JSON-RPC. Additionally, MCP uses stateful sessions to coordinate context sharing and sampling operations.

The core components of MCP architecture include:

Host Process: Acts as a container or coordinator for multiple client instances. It manages the lifecycle and security policies (such as permissions, user authorization, and approval requirements). The host oversees model integration within each client, aggregates and merges necessary context, and essentially serves as a control tower for incoming AI “flights,” deciding which clients operate, who controls them, and which servers (runways) they connect to.
Client Instances: Each client runs within the host process. It negotiates capabilities with a specific server and handles all message traffic between the client and the server. Clients enforce security boundaries to prevent unauthorized access to other clients’ resources. Each client connects to one server on a one-to-one basis.
Servers: Lightweight programs that provide access to data and functionality from external systems and tools. These may include local resources (e.g., files, databases) or cloud-based services accessed via APIs (e.g., Salesforce, Box). Servers expose their capabilities using standardized MCP definitions, enabling clients to interact with tools, resources, and prompts.
Transport Layer: The mechanism that handles communication between clients and servers. MCP supports two main transport methods:
- STDIO (Standard Input/Output): Used for local integrations where the client and server operate on the same machine. It’s simple and efficient for local communications.
- HTTP + SSE (Server-Sent Events): Used for remote connections. The client connects via HTTP, and the server sends continuous event-based messages over an open SSE channel.

There are also some basic concepts in the MCP architecture:

Tools: Structured “functions” that LLMs can call to perform certain actions. For example, a weather API for retrieving weather information can be defined as a tool.

Resources: Data sources that LLMs can access. They can be likened to GET endpoints in REST APIs, which are used to access specific information on a web server. Sources provide data without significant processing or side effects.

Prompts: Predefined templates for using tools or resources in an optimal way. These templates contain instructions to help the AI perform specific tasks.

Sampling: This feature allows servers to request LLM completion from the client, reversing the traditional client-server relationship. This gives clients full control over model selection, hosting, privacy and cost management.

Roots: Defines a specific location in the host system’s file system or environment that the server can interact with. This helps determine the server’s access limits.
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MCP’s architecture is designed to enable different AI applications and external systems to interact in a modular and secure way. The clear separation between client and server allows for centralized management of security policies, while the standardized communication protocol increases the interoperability of components created by different developers. The USB-C analogy simplifies the idea of different devices being able to communicate through a common port, making MCP’s role in the AI ecosystem understandable. Just as different computer peripherals (keyboards, mice, printers, etc.) can be seamlessly connected through the USB-C port despite being developed by different manufacturers, MCP enables different AI applications (Anthropic’s Claude, Microsoft’s Copilot, open source tools) to communicate with different external systems (GitHub, Slack, databases, custom APIs) through a standard protocol. This simplifies the integration process, shortens development time and allows the overall AI ecosystem to grow faster.

MCP Application Areas: In Which Sectors and How Is It Used?

The Model Context Protocol (MCP) has the potential to be used in a wide variety of sectors and application areas. Here are some of them:

Customer Support: MCP can significantly enhance the capabilities of customer support chatbots. By integrating LLMs with customer support applications via MCP, they can access customer ticketing data and use this data to better understand user issues and provide more effective solutions. For example, by connecting to systems like Salesforce, a customer service platform, or Box, a file-sharing service, chatbots can gain contextual information about the customer’s past interactions and current situation.
Enterprise AI Search: Organizations can use MCP to support AI assistants that answer their employees’ questions in natural language. By integrating LLMs with companies’ file storage systems via MCP, they can understand the content of documents and use this content to answer user questions, even providing links to relevant documents. Assembly’s DoraAI product is an example of such an application.
Recruitment Processes: MCP can also play a significant role in recruitment processes. By integrating AI agents with Applicant Tracking Systems (ATS) via MCP, LLMs can access candidate information such as resumes, cover letters, and LinkedIn profiles, and summarize this information to assist recruiters. Peoplelogic’s “Noah” agent is an example of this type of application.
Development Environments: MCP can be used to improve software development processes. For example, code editors like Cursor can directly query databases, manage project configurations, and interact with various development tools such as GitHub, Notion, and Stripe via MCP. Additionally, thanks to integration with 3D modeling software like Blender, AI models like Claude can directly control Blender, facilitating prompt-based 3D modeling and scene creation processes.
PydanticAI Integration: PydanticAI supports MCP in three different ways: agents can use their tools by connecting to MCP servers as MCP clients, agents can be used within MCP servers, and custom MCP servers can be developed within PydanticAI (e.g., a secure Python interpreter).
Other Application Areas: MCP has potential uses for any AI application that needs access to data sources and tools. For example, it can be used in the healthcare sector for AI applications that need secure access to patient data.

The wide range of application areas for MCP significantly increases the potential of AI in various sectors. Thanks to a standard connection protocol, AI models can access not only general information but also specific business processes and data. This paves the way for the development of more intelligent, contextually relevant, and actionable AI applications. MCP is expected to have a significant impact, especially in areas such as customer interaction, information management, and automation. Traditionally, for an AI application to perform a specific task, it needed to be directly integrated with the data and tools specific to that task. This meant developing custom solutions for each new use case. MCP changes this approach by allowing AI applications to connect to different data sources and tools in a standard way, simplifying the development process and offering a broader range of applications. For example, an AI assistant in a financial institution can access both customer account information and analyze market data, and even perform certain transactions, thanks to MCP.

Advantages Offered by MCP: Efficiency, Integration, and Beyond

The Model Context Protocol (MCP) provides a range of significant benefits for those developing and utilizing artificial intelligence applications:

Simplified Integration Process: By providing a single, standardized protocol, MCP drastically streamlines the integration process between LLM providers and SaaS applications. This eliminates the need for bespoke connectors, transforming a complex “M×N” problem into a more manageable “M+N” challenge.
Enhanced LLM Efficiency: MCP standardizes context management, minimizing unnecessary processing for LLMs. Furthermore, it offers a structured way for LLMs to maintain, update, and retrieve context, enabling them to autonomously manage and advance workflows.
Strengthened Security and Compliance: MCP offers standardized governance over how context is stored, shared, and updated across different environments. The protocol’s architecture allows for the enforcement of security policies at the protocol layer.
Flexibility Across LLM Providers: MCP enables seamless switching between different LLM providers and vendors. This allows users to select the LLM best suited to their needs and change providers as required.
Expanding Integration Landscape: MCP offers a growing list of pre-built integrations that LLMs can connect to directly. This makes it easy for developers to connect to commonly used data sources and tools.
Data Security Best Practices: MCP supports best practices for securely keeping data within the user’s infrastructure. This ensures that sensitive data is processed securely by AI applications.
Rapid Prototyping and Workflow Orchestration: MCP offers developers an excellent experience for rapid prototyping and building context-aware applications.
Reproducibility: MCP enhances the reproducibility of AI applications by ensuring that all necessary details (datasets, environment specifications, hyperparameters) are located in a single place.
Standardization and Collaboration: MCP facilitates cross-organizational sharing among companies building specialized AI tools or proprietary data sources. It also ensures that open-source communities like Hugging Face or GitHub can rely on consistent metadata standards to streamline model sharing and discovery.
Bidirectional Communication and Tool Discovery: MCP offers users a wide range of services they can connect to from AI clients. It allows developers to expose their services and APIs to numerous AI clients with a single protocol implementation. Additionally, it enables bidirectional communication, tool discovery, and a rich foundational feature set between servers and clients.
“AI-Native” Design: Unlike legacy standards like OpenAPI, GraphQL, or SOAP, MCP is specifically designed for the needs of modern AI agents.
Strong Backing: MCP has a significant supporter in Anthropic and boasts a comprehensive specification.
Network Effects: MCP’s open-source nature encourages broad community adoption and development.

The advantages offered by MCP signal a significant potential transformation in AI development and deployment processes. Easier integration reduces development costs, while increased efficiency leads to faster and more effective AI solutions. Security and compliance standards enable AI to work securely with sensitive data, and flexibility across LLM providers allows users to choose the most suitable technology for their needs. The combination of these benefits makes MCP a critical technology for the future of the AI ecosystem. Just as the internet became widespread thanks to the TCP/IP protocol, AI applications can be expected to reach wider audiences and be used in more areas thanks to MCP. The ability for different AI models and external systems to speak a common “language” fosters innovation and accelerates the development of next-generation AI-powered products and services. For example, a retail company, thanks to MCP, can use different LLMs to both improve customer service, optimize inventory management, and even create personalized marketing campaigns.

Potential Limitations and Considerations for MCP

While the Model Context Protocol (MCP) offers numerous advantages, it also presents potential limitations and crucial points to consider:

Novel Protocol Status: MCP is still in active development, and therefore, certain limitations exist. Its not-yet-full industry-wide adoption can also be seen as a drawback.
Tool Count Limitation: In some clients, such as Cursor, the number of active MCP servers can limit the tools sent to the LLM, currently capped at the first 40. This might prevent the utilization of all available tools in certain scenarios.
Remote Development Challenges: As Cursor communicates directly with MCP servers from the local machine, MCP servers might not function correctly in SSH or other remote development environments. This can restrict MCP usage in remote development scenarios.
Lack of Resource Support (in Some Clients): In certain clients like Cursor, the “resources” feature offered by MCP servers is not yet fully supported. Resources enable more complex interactions with external data sources, and the absence of this support can limit some use cases.
Absence of Standardized Error Handling: MCP does not implement a standardized error handling framework or response status codes. This can lead to different API providers using varying error handling methods, potentially causing inconsistencies in integration processes.
Security Concerns: The powerful capabilities offered by MCP can introduce certain security risks:
- Prompt Injection Attacks: As LLMs accept seemingly reliable input, malicious prompts can lead to unauthorized tool calls or the leakage of sensitive data.
- Unauthorized Access: Without proper visibility and control mechanisms, AI assistants could gain unauthorized access to or modify sensitive data.
- Lack of Approval Workflows: The absence of built-in approval workflows for critical operations (database changes, financial transactions, etc.) can create risks in situations requiring human intervention.
- Limited Audit Trails and Monitoring: Insufficient tracking of requests can complicate security investigations and compliance reporting.
- Privilege Management Challenges: Managing access across multiple MCP servers with varying security needs can become complex.
- Token Theft and Account Takeover: If OAuth tokens stored on MCP servers are compromised, attackers could access a user’s email history, send emails, delete important communications, or exfiltrate data on a large scale.
- MCP Server Security: MCP servers are high-value targets as they store authentication tokens for multiple services. Compromising these servers can grant widespread access to all connected services.
- Excessive Permission Scope and Data Aggregation: MCP servers may request broad permission scopes, posing significant privacy and security risks. Centralizing multiple service tokens creates unprecedented data aggregation potential, enabling attackers to perform cross-service correlation attacks.
Local Usage Focus (Initially): MCP’s initial primary focus on development and enterprise integration might limit its usability for individual users. Claude Desktop’s support for local MCP server testing currently requires a Claude for Work subscription.
Hardware Implications: As MCP is not a model optimization framework, it doesn’t have a direct relationship with the user’s hardware specifications.
Fundamental Limitations for Consumers: Its initial orientation towards enterprise and developer use, and the requirement for access to commercial AI models (like Claude), can create certain limitations for consumers. Furthermore, it is not designed to improve local model performance.

While MCP offers significant advantages in streamlining AI integration and boosting efficiency, it also brings certain limitations and security risks. Security vulnerabilities, particularly prompt injection, raise serious concerns about the potential misuse of AI applications. It is crucial for developers and users to be aware of these risks and implement appropriate security measures. The protocol’s ongoing maturation phase may also mean that some features are not yet fully supported or that stability issues may arise. The flexibility and connectivity capabilities of MCP can also create new avenues for malicious actors to infiltrate systems or perform unauthorized actions. For example, an attacker could create a malicious MCP server or compromise an existing one, gaining access to a user’s sensitive data or making changes to their systems through the AI application. Therefore, for MCP to be used securely, both the protocol itself and the applications and servers that use it must be carefully designed and protected against vulnerabilities. Users should also be vigilant about which MCP servers they connect to and what permissions they grant.

Future and Development Directions of MCP

The Model Context Protocol (MCP) is a continuously evolving technology with significant future potential. Its development roadmap aims to further enhance its capabilities and expand its adoption across a broader range of use cases:

Development Roadmap:
- Remote Connectivity: Future developments for MCP will include establishing secure connections to MCP servers using OAuth 2.0, facilitating service discovery, and supporting stateless operations. This will ensure more secure and flexible interactions between AI clients and servers.
- Developer Resources: To encourage easier adoption and contribution by developers, the development of reference client implementations and a streamlined protocol feature proposal process are planned.
- Deployment Infrastructure: Standardized packaging formats, simplified setup tools, server virtualization for security, and a centralized server registry are being developed to ease the deployment and management of MCP servers. These improvements will help make MCP more accessible and reliable.
- Agent Capabilities: MCP will focus on providing advanced support for hierarchical agent systems, interactive user workflows, and real-time streaming of results from long-running operations. This indicates MCP’s commitment to facilitating more complex and interactive AI agent interactions.
- Ecosystem Expansion: The goals include community-driven standard development with equal participation among AI providers, support for additional modalities beyond text (such as image and audio), and the formal standardization of the protocol. These are crucial steps for MCP to become a truly open and widely adopted standard.
Widespread Adoption: The increasing number of data and system vendors beginning to integrate MCP into their products demonstrates the protocol’s rapid proliferation. Even major model vendors like OpenAI, Google Cloud Platform, AWS, and Microsoft are adding support for the protocol. This situation is highly promising for MCP’s future.
Potential to Become a De-Facto Standard: With growing interest and support, MCP has the potential to become a de-facto standard for next-generation agent and tool interactions.

The future of MCP looks exceptionally bright. The interest and support for the protocol, particularly from major technology companies and open-source communities, indicate that MCP will continue to play a significant role in the AI ecosystem. The improvements and new features outlined in the development roadmap will further enhance MCP’s capabilities and enable its adoption across a wider range of applications. In the future, MCP is expected to play a critical role in enabling AI applications to evolve from simply accessing information to becoming intelligent agents capable of performing more complex tasks. The future success of MCP will largely depend on the support of the developer community and major players in the industry. The protocol’s open-source nature and the backing of a strong supporter like Anthropic are positive indicators in this regard. In the future, MCP’s ability to support not only text-based interactions but also image, audio, and other modalities could pave the way for AI applications to be used in much richer and more diverse scenarios. Furthermore, the emphasis on security and privacy is vital for MCP to be recognized as a trusted standard.

Conclusion: The Role of MCP in the Artificial Intelligence Ecosystem

The Model Context Protocol (MCP) plays a critical role in standardizing the connection between artificial intelligence models and external data sources and tools. The advantages offered by the protocol, such as ease of integration, increased efficiency, and security, significantly simplify the development and deployment of AI applications. While existing limitations and security risks must be considered and appropriate measures taken, examining MCP’s future potential and development directions reveals its capacity to usher in a new era within the artificial intelligence ecosystem.

The Model Context Protocol (MCP) represents a significant turning point in the artificial intelligence ecosystem. By standardizing how AI models interact with real-world data and tools, it paves the way for the development of more intelligent, contextually aware, and actionable AI applications. Although still in the early stages of its development, the potential offered by MCP can significantly expand the use of AI across various sectors and form the foundation for next-generation AI-driven innovation. Understanding and adopting MCP can be critical for businesses and technology leaders seeking a competitive edge in the future. MCP has the potential to transform AI from merely a source of information into an active tool capable of solving real-world problems. This can enable businesses to increase their operational efficiency, improve customer experiences, and create entirely new business models. However, to fully realize this potential, security risks must be carefully managed, the protocol must be continuously developed, and widespread community adoption is essential.