Introduction
Yesterday, the AI-related token $Dark on Solana was listed on Binance Alpha, with its market cap reaching around $40 million so far.
In the latest crypto AI narrative, $Dark is closely related to "MCP" (Model Context Protocol), which is also an area that Web2 tech companies like Google are currently focusing on and exploring.
However, at present, there are few articles that can clearly explain the concept and narrative impact of MCP.
The following is an in-depth article about the MCP protocol by Mohamed ElSeidy, a researcher at Alliance DAO, which explains the principles and positioning of MCP in very simple language, which may help us quickly understand the latest narrative.
TechFlow has compiled the full text.
During my years at Alliance, I witnessed countless founders building their own specialized tools and data integrations, which were embedded into their AI agents and workflows. However, these algorithms, formalization, and unique datasets were locked behind custom integrations, rarely used by anyone.
With the emergence of the Model Context Protocol (MCP), this situation is rapidly changing. MCP is defined as an open protocol that standardizes how applications communicate with large language models (LLMs) and provide context. A metaphor I really like is: "For AI applications, MCP is like USB-C in hardware"; it is standardized, plug-and-play, versatile, and transformative.
Why Choose MCP?
Large language models (such as Claude, OpenAI, LLAMA, etc.) are very powerful, but they are limited by the information they can currently access. This means they usually have a knowledge cutoff point, cannot independently browse the web, and cannot directly access your personal files or specialized tools unless some form of integration is performed.
In particular, before this, developers faced three main challenges when connecting LLMs to external data and tools:
- Integration Complexity: Building separate integrations for each platform (such as Claude, ChatGPT, etc.) requires repeated effort and maintaining multiple code bases.
- Tool Fragmentation: Each tool function (such as file access, API connections, etc.) requires its own dedicated integration code and permission model.
- Limited Distribution: Specialized tools are restricted to specific platforms, limiting their coverage and impact.
- MCP solves these problems by providing a standardized method that allows any LLM to securely access external tools and data sources through a universal protocol. Now that we understand the role of MCP, let's see what people are building with it.
- What are people building with MCP?
- The MCP ecosystem is currently in a period of innovation explosion. Here are some of the latest examples of developers showcasing their work that I discovered on Twitter:
- AI-Driven Storyboard: An MCP integration that allows Claude to control ChatGPT-4o to automatically generate a complete Ghibli-style storyboard without any human intervention.
- ElevenLabs Voice Integration: An MCP server that allows Claude and Cursor to access the entire AI audio platform through simple text prompts. This integration is powerful enough to create voice agents that can make outbound calls. This demonstrates how MCP can extend current AI tools into the audio domain.
- Browser Automation with Playwright: An MCP server that enables AI agents to control web browsers without screenshots or visual models. This creates new possibilities for web page automation by standardizing LLM direct browser interaction.
- Personal WhatsApp Integration: A server connecting a personal WhatsApp account, enabling Claude to search messages and contacts, and send new messages.
- Airbnb Search Tool: An Airbnb apartment search tool that demonstrates the simplicity of MCP and the ability to create practical applications that interact with web services.
- Robot Control System: An MCP controller for robots. This example bridges the gap between LLMs and physical hardware, showcasing MCP's potential in IoT applications and robotics.
- Google Maps and Local Search: Connecting Claude to Google Maps data to create a system that can find and recommend local businesses (like coffee shops). This extension enables AI assistants to provide location-based services.
- Blockchain Integration: The Lyra MCP project brings MCP functionality to StoryProtocol and other web3 platforms. This allows interaction with blockchain data and smart contracts, opening new possibilities for AI-enhanced decentralized applications.
What is particularly noteworthy about these examples is their diversity. In the short time since MCP's launch, developers have created integrations covering creative media production, communication platforms, hardware control, location services, and blockchain technology. These various different applications follow the same standardized protocol, demonstrating MCP's versatility and its potential to become a universal standard for AI tool integration.
To view a comprehensive collection of MCP servers, you can visit the official MCP server repository on GitHub. Before using any MCP server, please carefully read the disclaimers and be cautious about what you run and authorize.
Promise and Hype
When facing any new technology, it's worth asking: Is MCP truly transformative, or is it just another overhyped tool that will ultimately fade away?
After observing numerous startups, I believe MCP represents a genuine turning point in AI development. Unlike many trends that promise revolution but only bring incremental changes, MCP is a productivity enhancement that solves fundamental infrastructure issues hindering the entire ecosystem's development.
What makes it special is that it does not try to replace or compete with existing AI models, but instead makes them more useful by connecting them to the external tools and data they need.
Nevertheless, reasonable concerns about security and standardization still exist. As with any protocol in its early stages, we may see growing pains as the community explores best practices in auditing, permissions, authentication, and server verification. Developers need to trust the functionality of these MCP servers without blindly trusting them, especially as they become more abundant. This article discusses some recent vulnerabilities exposed by blindly using uncarefully reviewed MCP servers, even when running locally.
The Future of AI is Contextualization
The most powerful AI applications will no longer be standalone models, but ecosystems of specialized capabilities connected through standardized protocols like MCP. For startups, MCP represents an opportunity to build specialized components that fit into these growing ecosystems. It's an opportunity to leverage your unique knowledge and capabilities while benefiting from the massive investment in foundational models.
Looking ahead, we can expect MCP to become a fundamental component of AI infrastructure, just as HTTP is to the web. As the protocol matures and adoption grows, we will likely see the emergence of dedicated MCP server markets that enable AI systems to leverage almost any imaginable capability or data source.
Has your startup attempted to implement MCP? I would love to hear about your experiences in the comments. If you have built something interesting in this field, please contact us via @alliancedao and apply.
Appendix
For those interested in understanding how MCP actually works, the following appendix provides a technical breakdown of its architecture, workflow, and implementation.
Behind MCP
Similar to how HTTP standardized web access to external data sources and information, MCP does this for AI frameworks, creating a universal language that enables different AI systems to communicate seamlessly. Let's explore how it does this.
MCP Architecture and Process
The main architecture follows a client-server model, with four key components working together:
- MCP Host: Includes desktop AI applications like Claude or ChatGPT, IDEs like cursorAI or VSCode, or other AI tools that need access to external data and functions.
- MCP Client: A protocol processor embedded in the host, maintaining a one-to-one connection with the MCP server.
- MCP Server: Lightweight programs that expose specific functions through a standardized protocol.
- Data Sources: Including files, databases, APIs, and services that the MCP server can securely access.
Now that we have discussed these components, let's look at their interaction in a typical workflow:
- User Interaction: Users ask questions or make requests in the MCP host (such as Claude Desktop).
- LLM Analysis: LLM analyzes the request and determines the need for external information or tools to provide a comprehensive response.
- Tool Discovery: The MCP client queries the connected MCP server to discover available tools.
- Tool Selection: The LLM decides which tools to use based on the request and available capabilities.
- Permission Request: The host requests permission from the user to execute the selected tools to ensure transparency and security.
- Tool Execution: After approval, the MCP client sends the request to the appropriate MCP server, which uses its specialized access to data sources to perform the operation.
- Result Processing: The server returns the results to the client, which formats them for LLM use.
- Response Generation: The LLM integrates external information into a comprehensive response.
- User Presentation: Finally, the response is presented to the end user.
The power of this architecture lies in each MCP server focusing on a specific domain while using standardized communication protocols. This way, developers do not need to rebuild integrations for each platform, but can develop tools once to serve the entire AI ecosystem.
How to Build Your First MCP Server
Now let's see how to implement a simple MCP server in just a few lines of code using the MCP SDK.
In this simple example, we want to extend Claude Desktop's capabilities to answer questions like "What coffee shops are near Central Park?" with information from Google Maps. You can easily expand this functionality to retrieve reviews or ratings. But for now, we focus on the MCP tool find_nearby_places, which will allow Claude to directly obtain this information from Google Maps and present the results conversationally.
As you can see, the code is very simple. First, it converts the query into a Google Maps API search, then returns the top results in a structured format. This way, the information is passed back to the LLM for further decision-making.
Now we need to let Claude Desktop know about this tool, so we register it in its configuration file as follows:
macOS path: ~/Library/Application Support/Claude/claude_desktop_config.jsonWindows path: %APPDATA%\Claude\claude_desktop_config.json
And that's it! You have now successfully extended Claude's functionality to find locations in real-time from Google Maps.
