Why Are All the AI Companies Talking About MCP? The Technology Agreement Reshaping Intelligent Agents.
21 April 2025 · Uncategorized ·
Summary
In 2025, the Model Context Protocol (MCP), an open protocol in the artificial intelligence field, emerged and quickly became a hot topic within the AI community despite initial lack of attention. Proposed by Anthropic, MCP aims to standardize interactions between AI models and external tools, data sources, and services, offering a client-server architecture that enables AI models to flexibly call upon tools and complete tasks—driving commercial applications for intelligent agents. This article explores the reasons behind MCP’s rise, its technical core, market impact, and future potential.
Translation
Title: Why Are All AI Companies Talking About MCP? Reshaping Intelligent Agent Technology Protocols
Amidst the rapidly evolving landscape of artificial intelligence technology in 2025, a protocol named Model Context Protocol (MCP) quietly appeared at the end of last year but recently became a trending topic within the AI community, sparking widespread discussion. Initially overlooked and even dismissed by some as merely a technical "gimmick," this open protocol proposed by Anthropic quickly rose to industry prominence, attracting attention from developers, businesses, and investors alike.
MCP provides a more standardized and structured interaction method between AI models and external tools, data sources, and various services, propelling a new wave of “intelligent agents” (AI Agents) towards commercial viability. Why did this technology protocol, introduced at the end of 2023, only recently gain traction on discussion boards?
Since Anthropic released an initial design for MCP in November 2024, it has been viewed as a brand-new HTTP protocol within the AI realm—MCP is considered a standardized "bridging technology" that can be used to connect various external APIs and AI models, including large language models like Claude and ChatGPT. Its key feature lies in its “client-server architecture,” as described in official documentation: “MCP employs a client-server architecture allowing a host application to connect to multiple servers simultaneously.” By enabling AI models to act as clients or agents managing multiple services, the system can be extended on a standardized foundation. This model aims to transform AI from a passive mode that only receives user input and returns results into an entirely new paradigm where it actively calls upon tools and completes tasks—essentially embodying intelligent agents with greater autonomy.
The emergence of MCP is not coincidental; rather, it’s a direct response to current bottlenecks in AI development. As large language models like GPT and Claude have excelled in natural language processing, their limitations are becoming increasingly apparent – while these models can generate fluent text, they struggle when interacting with the external world. For example, developers often need to manually write complex code for each scenario when querying databases, calling APIs, or executing system commands; this is not only time-consuming and laborious but also leads to fragmentation of application scenarios.
Anthropic’s introduction of MCP aims to address these issues directly. According to Anthropic's official documentation, the goal of MCP is to allow AI models to flexibly "call tools" or "complete tasks," much like humans do, through standardized interactions. It provides a unified intermediary layer that connects models with external resources—whether databases, file systems, or hardware devices. This not only enhances the practicality of AI but also paves the way for commercial applications of artificial intelligence agents. In essence, MCP represents a turning point in AI's evolution from simple language generation to broader intelligent application domains.
While still in its early stages with limited practical use cases and developer understanding when launched in November 2024, the situation began to change by early 2025. An increasing number of MCP server developers emerged, along with related development tools on platforms like GitHub, Google Drive, and Slack; developers started recognizing the practicality of MCP. Within just a few months, the number of MCP servers exceeded 250, rapidly enriching the ecosystem. The accumulation of these use cases shifted developer sentiment from "sounds interesting" to “this is actually usable,” driving increased attention towards MCP.
This surge in popularity can be attributed both to startups seeking technological breakthroughs and businesses looking for more efficient and flexible AI solutions—MCP effectively captured a crucial turning point in AI development by providing a standardized, open way to connect AI with external systems, thereby promoting the application and adoption of intelligent agents.
Principles and Advantages
- Standardization & Openness: MCP is not dependent on specific models; it can work with various AI models without being restricted by any single vendor's technology ecosystem. This means developers don’t need to write custom code for each integration scenario, saving time and avoiding potential errors. Furthermore, MCP has the potential to foster a brand-new ecosystem—as mentioned in an a16z report, once MCP becomes widespread, non-professionals may be able to easily combine AI with various applications, opening up new commercial possibilities for intelligent agents.
- Improved Development Efficiency: By providing standardized interaction methods, developers can more effectively integrate AI models with external systems, reducing the workload of manually writing code and improving development efficiency.
- Promotes Cross-Domain Applications: MCP’s openness encourages fusion between different technologies and systems, fostering innovative cross-domain applications. For example, in smart home environments, MCP could allow users to control device functions like light brightness through simple commands.
Challenges & Future
Despite demonstrating significant potential, several challenges need addressing:
- Computational Resources & Efficiency: Collaboration among models is relatively resource-intensive; this can become a bottleneck particularly in situations with limited environmental constraints or insufficient hardware conditions.
- Communication Protocol Optimization: MCP’s communication protocol still needs continuous optimization to improve efficiency and accuracy.
- Competition & Standardization: While there are currently no strong proprietary alternatives that directly replace the open MCP protocol, technologies like OpenAI's "Work with Apps" have already implemented some functionalities. Sagar Batchu predicts “schema wars” (protocol disputes) may emerge in the future until a unified standard takes shape.
- Model Evolution: If future AI models can understand external systems without relying on any intermediary layer, MCP’s role might be marginalized. However, this is unlikely to happen in the short term; enterprise deployments will still require universal protocols with auditing and security management functions—this remains an important core value of MCP for now.
Conclusion
In 2025, the Model Context Protocol (MCP), an open protocol in the artificial intelligence field, emerged and quickly became a hot topic within the AI community despite initial lack of attention. Proposed by Anthropic, MCP aims to standardize interactions between AI models and external tools, data sources, and services, offering a client-server architecture that enables AI models to flexibly call upon tools and complete tasks—driving commercial applications for intelligent agents. This article explores the reasons behind MCP’s rise, its technical core, market impact, and future potential.
Translation
Title: Why Are All AI Companies Talking About MCP? Reshaping Intelligent Agent Technology Protocols
Amidst the rapidly evolving landscape of artificial intelligence technology in 2025, a protocol named Model Context Protocol (MCP) quietly appeared at the end of last year but recently became a trending topic within the AI community, sparking widespread discussion. Initially overlooked and even dismissed by some as merely a technical "gimmick," this open protocol proposed by Anthropic quickly rose to industry prominence, attracting attention from developers, businesses, and investors alike.
MCP provides a more standardized and structured interaction method between AI models and external tools, data sources, and various services, propelling a new wave of “intelligent agents” (AI Agents) towards commercial viability. Why did this technology protocol, introduced at the end of 2023, only recently gain traction on discussion boards?
Since Anthropic released an initial design for MCP in November 2024, it has been viewed as a brand-new HTTP protocol within the AI realm—MCP is considered a standardized "bridging technology" that can be used to connect various external APIs and AI models, including large language models like Claude and ChatGPT. Its key feature lies in its “client-server architecture,” as described in official documentation: “MCP employs a client-server architecture allowing a host application to connect to multiple servers simultaneously.” By enabling AI models to act as clients or agents managing multiple services, the system can be extended on a standardized foundation. This model aims to transform AI from a passive mode that only receives user input and returns results into an entirely new paradigm where it actively calls upon tools and completes tasks—essentially embodying intelligent agents with greater autonomy.
The emergence of MCP is not coincidental; rather, it’s a direct response to current bottlenecks in AI development. As large language models like GPT and Claude have excelled in natural language processing, their limitations are becoming increasingly apparent – while these models can generate fluent text, they struggle when interacting with the external world. For example, developers often need to manually write complex code for each scenario when querying databases, calling APIs, or executing system commands; this is not only time-consuming and laborious but also leads to fragmentation of application scenarios.
Anthropic’s introduction of MCP aims to address these issues directly. According to Anthropic's official documentation, the goal of MCP is to allow AI models to flexibly "call tools" or "complete tasks," much like humans do, through standardized interactions. It provides a unified intermediary layer that connects models with external resources—whether databases, file systems, or hardware devices. This not only enhances the practicality of AI but also paves the way for commercial applications of artificial intelligence agents. In essence, MCP represents a turning point in AI's evolution from simple language generation to broader intelligent application domains.
While still in its early stages with limited practical use cases and developer understanding when launched in November 2024, the situation began to change by early 2025. An increasing number of MCP server developers emerged, along with related development tools on platforms like GitHub, Google Drive, and Slack; developers started recognizing the practicality of MCP. Within just a few months, the number of MCP servers exceeded 250, rapidly enriching the ecosystem. The accumulation of these use cases shifted developer sentiment from "sounds interesting" to “this is actually usable,” driving increased attention towards MCP.
This surge in popularity can be attributed both to startups seeking technological breakthroughs and businesses looking for more efficient and flexible AI solutions—MCP effectively captured a crucial turning point in AI development by providing a standardized, open way to connect AI with external systems, thereby promoting the application and adoption of intelligent agents.
Principles and Advantages
- Standardization & Openness: MCP is not dependent on specific models; it can work with various AI models without being restricted by any single vendor's technology ecosystem. This means developers don’t need to write custom code for each integration scenario, saving time and avoiding potential errors. Furthermore, MCP has the potential to foster a brand-new ecosystem—as mentioned in an a16z report, once MCP becomes widespread, non-professionals may be able to easily combine AI with various applications, opening up new commercial possibilities for intelligent agents.
- Improved Development Efficiency: By providing standardized interaction methods, developers can more effectively integrate AI models with external systems, reducing the workload of manually writing code and improving development efficiency.
- Promotes Cross-Domain Applications: MCP’s openness encourages fusion between different technologies and systems, fostering innovative cross-domain applications. For example, in smart home environments, MCP could allow users to control device functions like light brightness through simple commands.
Challenges & Future
Despite demonstrating significant potential, several challenges need addressing:
- Computational Resources & Efficiency: Collaboration among models is relatively resource-intensive; this can become a bottleneck particularly in situations with limited environmental constraints or insufficient hardware conditions.
- Communication Protocol Optimization: MCP’s communication protocol still needs continuous optimization to improve efficiency and accuracy.
- Competition & Standardization: While there are currently no strong proprietary alternatives that directly replace the open MCP protocol, technologies like OpenAI's "Work with Apps" have already implemented some functionalities. Sagar Batchu predicts “schema wars” (protocol disputes) may emerge in the future until a unified standard takes shape.
- Model Evolution: If future AI models can understand external systems without relying on any intermediary layer, MCP’s role might be marginalized. However, this is unlikely to happen in the short term; enterprise deployments will still require universal protocols with auditing and security management functions—this remains an important core value of MCP for now.
Conclusion