Model Context Protocol and Qtum

A Brief Discussion on Qtum's latest AI developments and Model Context Protocol

In the rapidly evolving landscape of artificial intelligence, a significant challenge is ensuring that powerful models can effectively connect with all the available data and tools. This is where the Model Context Protocol (MCP) emerges as a transformative open standard, aiming to provide a universal language for AI systems. 

Qtum Ally is an MCP host that provides users with an efficient way to put two or more tasks together and manage them efficiently and with minimal input.

What does this mean? Say you’d like to use AI to find you an Airbnb rental in Phoenix, Arizona, for less than $1000.00 a week. Instead of spending hours going through each listing, you could have Qtum Ally do the work of finding properties that meet your criteria and then create a list so you can easily read through and pick valid options. Qtum Ally can even build the results into a PowerPoint or another viewing program if desired. 

Qtum Ally works together with different LLMs, MCP servers and clients to do tasks that used to be done by hand.

LLMs that offer MCP services use MCP hosts to make this happen. For example, a MCP host could use the Airbnb MCP server offered by Airbnb to search the listings and an e-mail service MCP to e-mail you results and also save a PowerPoint to show others. 

The MCP host itself will generate the PowerPoint and create the list, but the important part here is that the LLM will automate the MCP services being used to find the Airbnb listings and e-mail them to you (using an e-mail MCP host).

Qtum Ally offers a collection of AI utilities that allow you to use different LLM models like Qwen, ChatGPT 4o & 5, GLM 4.5 Air, DeepSeek, Claude, and Gemini. Bundled with this are a series of MCP hosts already pre-installed, and you can add new ones yourself easily.

There’s basically an MCP server for most use cases that make sense, for example, YouTube and Airbnb have an MCP server, and more are becoming available each day. Sometimes they’re not even being published by the original author, because anyone can create the server, and they get listed on MCP server directories.

In the past, a lot of these tasks could be done by writing various scripts, which was out of reach of the average person who didn’t have coding experience. 

So now that we’ve explained a little bit about what you can do with LLMs and MCP, how does it work?

What is the Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is an open standard and open-source framework introduced by Anthropic in November 2024 to standardize how AI systems, particularly large language models (LLMs), integrate and share data with external tools, systems, and data sources. It functions as a universal interface for reading files, executing functions, and handling contextual prompts.

The Problem MCP Solves: Before MCP, developers typically had to create custom integrations or "connectors" for each individual data source an AI model needed to access. This led to a "fragmented" and "N×M" data integration problem (this happens when LLMs have to connect to so many data sources that it becomes messy), making it difficult to scale AI applications that require real-time, external information. 

Furthermore, many valuable operational data (like current weather, hotel rates, or customer orders) cannot be "baked into" an LLM’s training data because it changes too rapidly.

How MCP Works: MCP operates on a client-server architecture:

• MCP Hosts: These are AI applications or interfaces, such as IDEs or AI tools (e.g., Claude Desktop), that initiate requests for context.

• MCP Clients: These protocol clients exist within the host and maintain a one-to-one connection with MCP servers. They convert user requests into a structured format for the protocol to process.

• MCP Servers: These are lightweight programs that expose specific capabilities through MCP, connecting to local or remote data sources, databases, or APIs. Examples include servers for file systems, Google Drive, Slack, GitHub, Spotify, and more.

Communication between clients and servers typically uses JSON-RPC 2.0 for structured message exchange and supports transport methods like standard input/output (stdio) for local connections and HTTP with Server-Sent Events (SSE) for remote connections.

MCP brings several significant advantages to AI integration:

• Standardization and Interoperability: It provides a consistent framework for AI models to interact with tools, promoting interoperability across different AI systems and data sources.

• Context-Awareness and Real-Time Data Access: MCP enables AI agents to access up-to-date, real-time operational data from external systems, which is crucial for informing responses and carrying out tasks that require current information.

• Flexibility and Scalability: It allows AI to access data regardless of its location (on-premise or cloud) and supports smoother transitions between different AI models, reducing vendor lock-in.

• Enhanced Security: MCP supports secure, bi-directional connections, ensuring sensitive data remains within organizational infrastructure and allowing for granular access controls and "AI data firewalls" to protect privacy and compliance. MCP servers can restrict access and enforce more granular controls than the upstream provider, limiting an attacker's access if a token is compromised.

• Reduced Development Effort: Developers can build against a standard protocol once, eliminating the need to create custom code for each data source.

Wider Industry Adoption: MCP has rapidly gained traction across the AI industry. It was initially developed and open-sourced by Anthropic. Soon after its release, OpenAI announced support for MCP across its Agents SDK, with plans for ChatGPT desktop app and Responses API. 

Google DeepMind also confirmed MCP support for its Gemini models. Microsoft has made significant investments, integrating MCP into Azure OpenAI, Microsoft Copilot Studio, and GitHub, and partnering with Anthropic to create an official C# SDK. 

Other major players like AWS have released their own specialized MCP Servers for code assistants, and platforms like Cloudflare allow deployment of remote MCP servers. Companies like Block, Apollo, Replit, Codeium, Sourcegraph, and Wix have also integrated MCP. It has even been likened to a "USB-C for AI" due to its aim to standardize AI-to-tool connections.

Qtum's Vision for AI

Cryptocurrency is the natural currency for a decentralized ecosystem. Our vision for Qtum AI is to offer computing power and useful AI tools to anyone who wants it. Our goal isn’t to collect personal data or modify models to be biased. We stated in 2016 that the goal of the Qtum Foundation is to promote the use of the Qtum Blockchain, and we have been working toward that goal for almost 10 years now. AI is one of the tools we plan to use to bring attention to our project.

Qtum’s foray into AI development started with a GPU data center acquisition in early 2024, followed by a rollout of image generators and chatbots powered by open source models. In early 2025, DeepSeek was making use of these GPU’s and offered on Qtum.ai 

Qtum's AI Ecosystem: 

Qtum development focuses on three areas: Qtum Core, Ecosystem, and Qtum AI. In 2024, Qtum acquired and brought online 10,000 Nvidia 3080Ti GPUs to power its AI initiatives. These initiatives include:

• Qtum Solstice: A conversational chatbot similar to ChatGPT, based on open-source models, designed to engage users in helpful and intelligent conversations. Launched in early 2024, this was the first AI utility launched by Qtum. This made use of open source models, and was replaced by Qtum DeepSeek in early 2025, and then integrated into Qtum Ally is late 2025.

• Qtum Qurator: A text-to-image generation model similar to Midjourney, also based on open-source models, enabling users to create images that typically require time-consuming work. Deployed in early 2024 and powered by Qtum’s GPU farm, this product has been replaced with the capabilities for image generation found in Qtum Ally.

• Qtum DeepSeek: Launched in early 2025 was Qtum’s implementation of DeepSeek and powered by a farm of Nvidia 3080Ti GPU’s. Completely free to use and did not collect personal information. This was later bundled into Qtum Ally.

• Qtum Ally: Released as an installed application for Windows and Mac in late 2025, Qtum Ally offers access to 12 various LLM’s. It is also configured to work with Model Context Protocol servers and hosts. Users can access even the latest paid ChatGPT features for free for a limited time. Qtum Ally can be downloaded and installed on the Qtum Github repository:

GitHub - qtumproject/ai-agent

Summary

Qtum’s implementation of MCP into various LLM’s offers users a powerful agent. Prior to MCP and LLM’s, users would need knowledge in coding to accomplish automation, but with Qtum Ally, users have access to 12 LLM’s and MCP functionality built in. Users don’t need to install as many applications to create their agent, it can all be done in one simple client. Qtum Ally doesn’t farm data to improve the LLM’s, it just offers them as a service, and there’s no cost for the service. All of this is powered by Qtum’s GPU farm.

Model Context Protocol allows users to automate practically anything they can think of, because it’s the LLM like ChatGPT or DeepSeek that’s using logic to control and make the MCP hosts work together. If a user wanted a social media account to talk to people and send follow up e-mails to them based on their conversation, it can do that easily. It can even build reports to show you how the progress is going.