MCP: A New Era for AI Agent Tool Interaction

Understanding MCP

Definition and Origins

MCP, or Model Context Protocol, is a standardized protocol introduced by Anthropic in November 2024. It addresses the fragmented interaction between AI models and external tools and data. Often likened to a ‘USB-C for AI,’ MCP offers a unified interface that allows AI agents to seamlessly access external resources like databases, file systems, websites, and APIs without needing complex, custom-built adaptation code for each tool.

If APIs are the universal language of the internet, connecting servers and clients, then MCP is the unifying language for AI tools, bridging the gap between intelligent agents and the real world. It empowers AI to manipulate tools through natural language, much like humans use smartphones. Tasks evolve from simple queries like ‘Tell me today’s weather’ to complex operations such as ‘Check the weather and remind me to take an umbrella’ or ‘Generate a 3D model and upload it to the cloud.’

Core Vision: MCP aims to enhance efficiency and empower AI agents with the ability to move beyond understanding to tangible action. This enables developers, businesses, and even non-technical users to customize intelligent agents, making them a bridge between virtual intelligence and the physical world.

The creation of MCP was not accidental. Anthropic, founded by former OpenAI members, recognized the limitations of LLMs, which are often confined to ‘information silos,’ with knowledge limited to their training data and lacking real-time access to external information. Following the success of the Claude series models in 2024, Anthropic realized the need for a universal protocol to unlock the full potential of AI. The open-source release of MCP quickly gained traction. By March 2025, over 2000 community-developed MCP Servers were online, covering scenarios ranging from file management to blockchain analysis, with over 300 GitHub projects involved and a growth rate of 1200%. MCP is not just a technical protocol but a community-driven collaboration framework.

MCP for the Everyday User

For individual users, MCP acts as a ‘magic key’ to AI, making complex intelligent tools accessible and user-friendly. It allows individuals to command AI using natural language to complete everyday tasks without requiring programming knowledge. Imagine instructing Claude to ‘Organize my schedule and remind me of tomorrow’s meetings.’ MCP automatically connects to calendars, emails, and reminder tools, completing the task in seconds. Or, consider saying, ‘Help me design a birthday card.’ MCP calls on a design server (like Figma), generates a personalized card, and saves it to the cloud. For non-technical users, MCP functions as an invisible super-assistant, transforming tedious operations into simple conversations, making technology genuinely serve life.

• Simple Understanding: MCP acts as a smart assistant, upgrading your AI helper from ‘just chatting’ to ‘getting things done,’ helping you manage files, plan your life, and even create content.
• Actual Value: It transforms AI from an inaccessible technology into a personal life assistant, saving time, improving efficiency, and protecting privacy.

Broader Scenarios: From Chores to Creativity

MCP is more than just a tool; it represents a lifestyle change, enabling everyone to ‘customize’ their AI assistant without needing expensive professional services. For the elderly, MCP can simplify operations—saying ‘Remind me to take my medicine and notify my family’ prompts AI to complete the task automatically, enhancing independence. MCP extends beyond simple tasks, stimulating creativity and addressing daily needs:

• Daily Management: Saying ‘List this week’s shopping and remind me’ allows MCP to check fridge stock and price comparison websites, generating a list and sending it via SMS.
• Learning and Growth: Students saying ‘Organize biology notes and make a study plan’ prompts MCP to scan notes, connect to learning platforms, and output a study plan and quiz questions.
• Interest Exploration: Learning to cook? Saying ‘Find Italian pasta recipes and ingredients’ allows MCP to search websites, check stock, and generate menus, saving the trouble of flipping through books.
• Emotional Connection: For birthdays, saying ‘Design a card and send it to mom’ lets MCP use Figma to design and send it via email.

Privacy and Control: Assurance for Users

Privacy is a top concern for individual users, and MCP’s permission control mechanism ensures users maintain complete control over data flow. For example, you can set permissions to ‘allow AI to read the calendar, but not touch photos,’ providing reliable authorization. Furthermore, MCP’s ‘sampling’ function allows users to review requests before AI executes sensitive tasks, such as analyzing bank statements, where users can confirm ‘only the latest month’s data’ is used. This transparency and control foster trust while maintaining convenience.

The Necessity of MCP

The limitations of LLMs have driven the need for MCP. Traditionally, the knowledge of AI models is limited to their training data, preventing access to real-time information. If an LLM wants to analyze the cryptocurrency market trends for March 2025, it must manually input data or write specific API calls, which can take hours or days. More seriously, developers face an ‘M×N problem’ when dealing with multiple models and tools—if there are 10 AI models and 10 external tools, 100 custom integrations are needed, exponentially increasing complexity. This fragmentation is inefficient and difficult to scale.

MCP addresses these barriers, reducing connections to N+M (only 20 configurations are needed for 10 models and 10 tools), allowing AI agents to flexibly call tools. Generating a report with real-time stock prices, which traditionally takes 2 hours, can be done in just 2 minutes with MCP.

Technical Architecture and Internal Operation of MCP

Technical Background and Ecological Positioning

MCP’s technical foundation is JSON-RPC 2.0, a lightweight, efficient communication standard that supports real-time bidirectional interaction, similar to the high performance of WebSockets. It operates through a client-server architecture:

• MCP Host: The user-interactive application, such as Claude Desktop, Cursor, or Windsurf, is responsible for receiving requests and displaying results.
• MCP Client: Embedded within the host, it establishes a one-to-one connection with the server, handles protocol communication, and ensures isolation and security.
• MCP Server: A lightweight program that provides specific functions, connecting local (such as desktop files) or remote (such as cloud APIs) data sources.

Transmission methods include:

• Stdio: Standard input/output, suitable for local fast deployment, such as file management, with latency as low as milliseconds.
• HTTP SSE: Server-sent events, supporting remote real-time interaction, such as cloud API calls, suitable for distributed scenarios.

Anthropic plans to introduce WebSockets by the end of 2025 to further improve remote performance. In the AI ecosystem, MCP has a unique position, differing from OpenAI’s Function Calling, which is tied to a specific platform, and LangChain’s tool library, which is developer-oriented. MCP serves developers, enterprises, and non-technical users through openness and standardization.

Architectural Design

MCP employs a client-server architecture, analogous to a restaurant setting: the customer (MCP host) wants to order food (data or actions), and the waiter (MCP client) communicates with the kitchen (MCP Server). To ensure efficiency and security, MCP assigns a dedicated client to each server, forming an isolated one-to-one connection. Key components include:

• Host: The user entry point, such as Claude Desktop, is responsible for initiating requests and displaying results.
• Client: The communication intermediary uses JSON-RPC 2.0 to interact with the server, managing requests and responses.
• Server: The function provider connects external resources and performs tasks, such as reading files or calling APIs.

Transmission methods are flexible:

• Stdio: Local deployment, suitable for quickly accessing desktop files or local databases, with latency as low as milliseconds, such as counting the number of txt files.
• HTTP SSE: Remote interaction, supporting cloud API calls, with strong real-time performance, such as querying weather APIs, suitable for distributed scenarios.
• Future Expansion: WebSockets or streamable HTTP may be implemented by the end of 2025, further improving remote performance and reducing latency.

Functional Primitives

MCP implements functions through three ‘primitives’:

1. Tools: Executable functions that AI calls to complete specific tasks. For example, a ‘currency conversion’ tool converts 100 RMB to 14 USD and 109 HKD in real-time (based on a fixed exchange rate in March 2025); a ‘search’ tool can query today’s movie showtimes.
2. Resources: Structured data used as context input. For example, reading a README file from a GitHub repository provides project background, or scanning a 10MB PDF file extracts key information.
3. Prompts: Predefined instruction templates that guide AI to use tools and resources. For example, a ‘summarize document’ prompt generates a 200-word summary, and a ‘plan itinerary’ prompt integrates calendar and flight data.

MCP supports a ‘sampling’ function where the server can request an LLM to process a task, and the user reviews the request and result, ensuring security and transparency. For example, if the server requests to ‘analyze file content,’ the user approves it, and AI returns a summary, ensuring sensitive data is not misused, enhancing security and transparency.

Communication Process

MCP’s operation includes four stages:

Consider the example of ‘querying desktop files’:

1. The user inputs ‘list my documents.’
2. Claude analyzes the request and identifies the need to call the file server.
3. The client connects to the server, and the user approves permissions.
4. The server returns a list of files, and Claude generates an answer.

Another example is ‘planning an itinerary’: the user inputs ‘arrange a Saturday trip,’ Claude discovers calendar and flight servers, obtains schedule and ticketing data, prompts integration, and returns ‘10:00 flight to Paris on Saturday.’

Why Should You Pay Attention to MCP?

Pain Points of the Current AI Ecosystem

The limitations of LLMs are evident:

• Information Silos: Knowledge is limited to training data and cannot be updated in real-time. For example, if an LLM wants to analyze Bitcoin transactions in March 2025, it needs to manually input data.
• M×N Problem: Integration between multiple models and tools is exponentially complex. For example, 10 models and 10 tools require 100 custom code integrations.
• Inefficiency: Traditional methods require embedding vectors or vector searches, which are computationally expensive and have long response delays.

These issues limit the potential of AI agents, making it difficult for them to move from ‘imagining’ to ‘doing.’

Breakthrough Advantages of MCP

MCP brings seven advantages through a standardized interface:

1. Real-Time Access: AI can query the latest data in seconds. Claude Desktop retrieves a list of files in 0.5 seconds via MCP, improving efficiency tenfold.
2. Security and Control: Data is accessed directly, eliminating the need for intermediate storage, with permission management reliability reaching 98%. Users can restrict AI to read only specific files.
3. Low Computational Load: Eliminates the need for embedded vectors, reducing about 70% of computing costs. Traditional vector searches require 1GB of memory, while MCP only needs 100MB.
4. Flexibility and Scalability: Reduces connections from N×M to N+M. 10 models and 10 tools only need 20 configurations.
5. Interoperability: An MCP Server can be reused by multiple models like Claude and GPT. One weather server serves global users.
6. Vendor Flexibility: Switching LLMs does not require restructuring the infrastructure.
7. Autonomous Agent Support: Supports AI dynamic access to tools, performing complex tasks. When planning a trip, AI can simultaneously query the calendar, book flights, and send emails, improving efficiency.

Importance and Impact

MCP is a catalyst for ecological change. It is like the Rosetta Stone, unlocking communication between AI and the external world. A pharmaceutical company integrated 10 data sources through MCP, reducing research query time from 2 hours to 10 minutes, improving decision-making efficiency by 90%. It also encourages developers to build universal tools, with one server serving the world, promoting the formation of an ecosystem.

Application Scenarios and Practical Cases of MCP

Diverse Application Scenarios

MCP’s applications are extensive:

1. Development and Productivity:
   • Code Debugging: Cursor AI debugs 100,000 lines of code via Browsertools Server, reducing error rates by 25%.
   • Document Search: Mintlify Server searches 1000 pages of documents in 2 seconds, saving 80% of time.
   • Task Automation: Google Sheets Server automatically updates 500 sales sheets, improving efficiency by 300%.
2. Creativity and Design:
   • 3D Modeling: Blender MCP reduces modeling time from 3 hours to 10 minutes, improving efficiency by 18 times.
   • Design Tasks: Figma Server assists AI in adjusting layouts, improving design efficiency by 40%.
3. Data and Communication:
   • Database Query: Supabase Server queries user records in real-time, with a response time of 0.3 seconds.
   • Team Collaboration: Slack Server automates message sending, saving 80% of manual operations.
   • Web Scraping: Firecrawl Server extracts data, doubling speed.
4. Education and Healthcare:
   • Educational Support: MCP Server connects to learning platforms, and AI generates course outlines, improving teacher efficiency by 40%.
   • Medical Diagnostics: Connects to patient databases, and AI generates diagnostic reports with an accuracy rate of 85%.
5. Blockchain and Finance:
   • Bitcoin Interaction: MCP Server queries blockchain transactions, improving real-time performance to the second level.
   • DeFi Analysis: Analyzes Binance big investor transactions, predicting profits, with an accuracy rate of 85%.

Specific Case Analysis

• Case Analysis: Claude scans 1000 files and generates a 500-word summary in just 0.5 seconds. Traditional methods require manually uploading files to the cloud, taking several minutes.
• Blockchain Application: AI analyzed Binance big investor transactions through MCP Server in March 2025, predicting potential profits, demonstrating its potential in the financial field.

MCP Ecosystem: Status and Participants

Ecosystem Architecture

The MCP ecosystem is beginning to take shape, covering four major roles:

1. Clients:
   • Mainstream Applications: Claude Desktop, Cursor, Continue.
   • Emerging Tools: Windsurf, LibreChat, Sourcegraph.
2. Servers:
   • Database Class: Supabase, ClickHouse, Neon, Postgres.
   • Tool Class: Resend, Stripe, Linear.
   • Creative Class: Blender, Figma.
   • Data Class: Firecrawl, Tavily, Exa AI.
3. Market:
   • mcp.so: Includes Servers, providing one-click installation.
   • Other Platforms: Mintlify, OpenTools.
4. Infrastructure:
   • Cloudflare: Hosting Servers, ensuring availability.
   • Toolbase: Optimizing latency.
   • Smithery: Providing dynamic load balancing.

Ecological Data

• Scale: By March 2025, MCP Server had increased from units in December 2024 to over 2000 units, a growth rate of over 1200%.
• Community: Over 300 GitHub projects participated, with Servers coming from developer contributions.
• Activity: An early Hackathon attracted over 100 developers, producing many innovative applications, such as shopping assistants and health monitoring tools.

Limitations and Challenges of MCP

Technical Bottlenecks

• Implementation Complexity: MCP contains prompts and sampling functions, increasing development difficulty. Tool descriptions need to be carefully written, otherwise LLM calls are prone to errors.
• Deployment Restrictions: Requires running on local terminals, manually starting the server, lacking one-click deployment or web applications, limiting remote scenarios.
• Debugging Challenges: Poor cross-client compatibility, insufficient logging support. For example, a server may work fine on Claude Desktop, but may fail on Cursor.
• Transmission Shortcomings: Only supports Stdio and SSE, lacking more flexible options like WebSockets, limiting remote real-time performance.

Ecological Quality Shortcomings

• Uneven Quality: Among over 2000 Servers, about 20% have stability issues or lack documentation, resulting in inconsistent user experiences.
• Insufficient Discoverability: Requires manually configuring server addresses, and the dynamic discovery mechanism is not yet mature, requiring users to search and test themselves.
• Scale Limitations: Compared to Zapier’s over 5000 tools or LangChain’s over 1000 tool library, MCP’s coverage is still insufficient.

Applicability Challenges in Production Environments

• Call Accuracy: Current LLM tool call success rate is about 90%, prone to failure in complex tasks.
• Customization Needs: Production Agents need to optimize system messages and architectures according to tools, and MCP’s ‘plug-and-play’ is difficult to meet.
• User Expectations: With the improvement of model capabilities, users have higher requirements for reliability and speed, and MCP’s generality may sacrifice performance.

Competition and Pressure from Alternative Solutions

• Proprietary Solutions: OpenAI’s Agent SDK provides higher reliability through deep optimization, potentially attracting high-end users.
• Existing Frameworks: LangChain’s tool library has established stickiness among developers, and MCP’s new ecosystem needs time to catch up.
• Market Comparison: OpenAI’s Custom GPTs have not been widely successful, and MCP needs to prove its unique value to avoid repeating mistakes.

Future Trends: MCP’s Evolution Path

Multi-Dimensional Path of Technical Optimization

• Protocol Simplification: Remove redundant functions, focusing on tool calls, reducing development barriers.
• Stateless Design: Support server-side deployment, introduce authentication mechanisms, solve multi-tenant problems.
• User Experience Standardization: Standardize tool selection logic and interface design to improve consistency.
• Debugging Upgrade: Develop cross-platform debugging tools, providing detailed logs and error tracking.
• Transmission Expansion: Support WebSockets and streamable HTTP to enhance remote interaction capabilities.

Strategic Direction of Ecological Development

• Marketplace Construction: Launch a platform similar to npm, integrating rating, search, and one-click installation functions to optimize server discovery.
• Web Support: Implement cloud deployment and browser integration, breaking away from local restrictions, targeting Web users.
• Business Scenario Expansion: Shift from coding tools to customer support, design, marketing, and other fields.
• Community Incentives: Encourage high-quality server development through bonuses, certifications, with the goal of reaching over 5000 Servers by the end of 2026.