An Important Step in Standardization of the AI Industry: MCP Protocol Analysis

Recently, a new protocol called MCP(Model Context Protocol) has attracted widespread attention in the AI community. Developed by Anthropic, this open-source protocol aims to provide a standardized interface for the interaction between AI models and external tools and data, and is hailed as the "USB-C of the AI field."

What is MCP?

The full name of MCP is Model Context Protocol (, which is a standardized protocol for connecting AI models with external resources. It allows AI models to access databases, file systems, APIs, and other external tools and data through a unified interface, without the need to develop separate adaptation code for each tool.

The core functions of MCP include:

• Unified Interface: Simplifies the integration of multiple models and tools
• Real-time data access: Query response time reduced to 0.5 seconds
• Security and Privacy Protection: Reliability of access control reaches 98%

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Technical Architecture of MC Protocol

MCP adopts a client-server architecture and mainly includes the following components:

• MCP host: User interaction applications, such as Claude Desktop
• MCP Client: Embedded in the host, responsible for communication with the server.
• MC Server: provides specific functionalities, connects to data sources

MCP supports two transmission methods: Stdio and HTTP SSE. The former is suitable for local rapid deployment, while the latter supports remote real-time interaction.

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Advantages of MC

Compared to traditional methods, MCP has the following prominent advantages:

1. Real-time: Latest data can be obtained within 0.5 seconds.
2. Security: Direct access to data without intermediate storage
3. Low computational load: No vector embedding, reducing computing costs by 70%.
4. Flexible and scalable: greatly simplifies the integration of models and tools.
5. Interoperability: One MCP server can be reused by multiple models.
6. Vendor Flexibility: Switch LLMs without restructuring infrastructure

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Application Scenarios of MC

MCP has demonstrated application potential in multiple fields:

• Development Workflow: such as Cursor AI code debugging
• 3D modeling: such as Blender MC
• Data query: like Supabase
• Productivity tools: such as Slack message automation
• Education and Healthcare: such as AI-assisted diagnosis
• Blockchain Finance: such as real-time transaction analysis

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MCP Ecological Status

As of March 2025, the MCP ecosystem has begun to take shape.

• Over 2000 MCP servers launched
• Participated in 300+ GitHub projects
• Mainstream clients include Claude, Cursor, etc.
• The server covers fields such as databases, tools, and creativity.
• Platforms like mcp.so provide one-click installation.

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Limitations and Challenges

MCP is still facing some challenges:

• Complexity of implementation: increased development difficulty
• Deployment Restrictions: Depend on local terminal execution
• Debugging difficulties: poor cross-client compatibility
• Uneven ecological quality: About 30% of servers have stability issues.
• Production environment applicability: Tool call accuracy is only 50%

Future Outlook

Possible future development directions for MC include:

• Protocol Simplification: Focus on core functions, lower the threshold
• Web Support: Achieve cloud deployment
• Ecological construction: Build a platform similar to npm
• Scene Expansion: Extending to More Business Areas

2025 will be a key year for the development of MCP, and whether it can become the foundation of the AI ecosystem deserves continuous attention.

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