Introduction
In the latest crypto AI narrative, $Dark is closely related to the "MCP" (Model Context Protocol), a field that recently has garnered attention and exploration from Web2 tech companies like Google.
However, there are currently not many articles that clearly explain the MCP concept and its narrative impact.
Below is an in-depth article by Mohamed ElSeidy, a researcher at Alliance DAO, that explains the principles and positioning of the MCP protocol in very accessible language. It may help us quickly understand the latest narrative.
DeepTide TechFlow has compiled the full text.
My Years at Alliance: Witnessing the Evolution of AI Integration
During my years at Alliance, I have witnessed countless founders build their own proprietary tools and data integrations, embedded into their AI agents and workflows. However, these algorithms, formalizations, and unique datasets were locked behind custom integrations, rarely used by anyone else.
This is rapidly changing with the emergence of the Model Context Protocol (MCP). MCP is defined as an open protocol that standardizes how applications communicate with large language models (LLMs) and provide context. One analogy I particularly like is: "For AI applications, MCP is like the USB-C of hardware"; it is standardized, plug-and-play, versatile, and transformative.
Why Choose MCP?
Large language models (such as Claude, OpenAI, LLAMA, etc.) are incredibly powerful, but they are limited by the information they can currently access. This means they often have knowledge cut-off points, unable to independently browse the web or directly access your personal files or proprietary tools without some form of integration.
In particular, developers previously faced three major challenges when connecting LLMs to external data and tools:
Integration Complexity: Building separate integrations for each platform (e.g., Claude, ChatGPT, etc.) requires redundant effort and maintaining multiple codebases.
Tool Fragmentation: Each tool function (e.g., file access, API connections, etc.) requires its own dedicated integration code and permission model.
Distribution Limitations: Proprietary tools are restricted to specific platforms, limiting their reach and impact.
MCP addresses these issues by providing a standardized method for any LLM to securely access external tools and data sources through a common protocol. Now that we understand what MCP does, let's see what people are building with it.
What Are People Building with MCP?
The MCP ecosystem is currently experiencing an explosion of innovation. Here are some recent examples I found on Twitter of developers showcasing their work:
AI-Driven Storyboarding: An MCP integration enabling Claude to control ChatGPT-4o, automatically generating full storyboards in a Ghibli style without any human intervention.
ElevenLabs Voice Integration: An MCP server allowing Claude and Cursor to access the entire AI audio platform with simple text prompts. This integration is powerful enough to create voice agents that can make outbound calls. It showcases how MCP extends current AI tools into the audio domain.
Browser Automation with Playwright: An MCP server enabling AI agents to control web browsers without taking screenshots or using visual models. This standardizes direct LLM control over browser interactions, creating new possibilities for web automation.
Personal WhatsApp Integration: A server connecting personal WhatsApp accounts, enabling Claude to search messages and contacts and send new messages.
Airbnb Search Tool: An Airbnb apartment search tool demonstrating MCP's simplicity and ability to create practical applications interacting 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 (e.g., 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 makes these examples particularly striking is their diversity. In the short time since MCP's launch, developers have created integrations spanning creative media production, communication platforms, hardware control, location services, and blockchain technology. These various applications follow the same standardized protocol, showcasing MCP's versatility and its potential to become a universal standard for AI tool integration.
To view a comprehensive collection of MCP servers, visit the official MCP server library on GitHub. Before using any MCP server, please read the disclaimer carefully and be cautious about what you run and authorize.
Promises vs. Hype
Faced with any new technology, it's worth asking: Is MCP truly transformative, or just another overhyped tool that will eventually 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 deliver incremental changes, MCP is a productivity enhancement that addresses infrastructure issues hindering the entire ecosystem's growth.
What makes it special is that it doesn't seek to replace or compete with existing AI models; instead, it makes them more useful by connecting them to the external tools and data they need.
Nevertheless, legitimate concerns about security and standardization persist. As with any protocol in its early stages, we may see growing pains as the community navigates best practices in auditing, permissions, authentication, and server validation. Developers need to trust the functionality of these MCP servers without blindly doing so, especially as they proliferate. This article discusses recent vulnerabilities exposed by blindly using unvetted MCP servers, even when run locally.
The Future of AI Lies in 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 tailored to these growing ecosystems. It's a chance to leverage your unique knowledge and capabilities while benefiting from the substantial investments in foundational models.
Looking ahead, we can expect MCP to become a fundamental component of AI infrastructure, much like HTTP is to the web. As the protocol matures and adoption grows, we are likely to see the emergence of a dedicated MCP server market, enabling AI systems to leverage virtually any conceivable capability or data source.
Has your startup tried implementing MCP? I'd love to hear about your experiences in the comments. If you've built something interesting in this space, please reach out to us via @alliancedao and apply.
Appendix: Understanding MCP's Behind-the-Scenes Mechanics
For those interested in understanding how MCP actually works, the following appendix provides a technical breakdown of its architecture, workflow, and implementation.
Behind the Scenes of MCP
Similar to how HTTP standardized how the web accesses external data sources and information, MCP does this for AI frameworks, creating a common language that allows different AI systems to communicate seamlessly. Let's explore how it does this.
MCP Architecture and Process
Alliance DAO Researcher: An In-Depth Understanding of the MCP Concept Behind DARK's Surge
The primary architecture follows a client-server model, with four key components working in tandem:
MCP Host: Includes desktop AI applications like Claude or ChatGPT, IDEs like cursorAI or VSCode, or other AI tools needing access to external data and functions.
MCP Client: A protocol handler embedded in the host, maintaining a one-to-one connection with an MCP server.
MCP Server: A lightweight program exposing specific functionalities through a standardized protocol.
Data Sources: Include files, databases, APIs, and services that MCP servers can securely access.
Now that we've discussed these components, let's look at their interactions in a typical workflow:
User Interaction: The user poses a question or request in the MCP host (e.g., Claude Desktop).
LLM Analysis: The LLM analyzes the request and determines that external information or tools are needed to provide a complete response.
Tool Discovery: The MCP client queries connected MCP servers to discover available tools.
Tool Selection: The LLM decides which tools to use based on the request and available functionalities.
Permission Request: The host requests permission from the user to execute the selected tools, ensuring transparency and security.
Tool Execution: Upon 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 use by the LLM.
Response Generation: The LLM integrates the 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 but using a standardized communication protocol. This way, developers don't need to rebuild integrations for each platform; they can develop tools once and have them serve the entire AI ecosystem.
How to Build Your First MCP Server
Now let's see how to implement a simple MCP server in 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?" using information from Google Maps. You can easily extend this functionality to fetch reviews or ratings. For now, we focus on the MCP tool find_nearby_places, which will allow Claude to obtain this information directly from Google Maps and present the results conversationally.
Alliance DAO Researcher: An In-Depth Understanding of the MCP Concept Behind DARK's Surge
As you can see, the code is quite simple. It first converts the query into a Google Maps API search and then returns the top result 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.json
Windows Path: %APPDATA%\Claude\claude_desktop_config.json
And that's it! You've successfully extended Claude's capabilities to find locations in real-time from Google Maps.
