From: aidotengineer
Kent C. Dodds, an educator in building excellent user experiences, notes a significant shift towards AI as the next frontier for user interaction [00:00:07]. His course platform, epicai.pro, focuses on how to build excellent user experiences with AI [00:00:16]. The core topic of this shift is how user interaction is changing and the role of product developers in reaching users within AI assistance platforms [00:00:24].
The Vision: Jarvis as the Ideal AI Assistant
To illustrate the potential of AI, Tony Stark’s AI assistant, Jarvis, from the Iron Man movies serves as a benchmark [00:01:25]. Jarvis’s capabilities in a practical example include:
- Compiling databases from various sources (e.g., SHIELD, FBI, CIA intercepts) [00:01:52].
- Initiating virtual crime scene reconstructions [00:02:01].
- Accessing public records [00:02:21].
- Analyzing thermogenic signatures and performing cross-dataset joins [00:02:26].
- Generating UIs on demand and interacting with them [00:03:12], [00:04:15].
- Showing related news articles [00:03:26].
- Creating flight plans [00:02:51].
- Answering the doorbell and displaying visitor information [00:03:31].
Jarvis represents an “awesome user experience” that leverages various input methods like typing, gestures, and voice AI [00:04:53]. While some of Jarvis’s abilities, like generating UIs, are already technically possible today [00:04:15], a key missing piece is the ability to create databases from classified information or display complex holographic interfaces [00:04:03].
The Problem: The Challenge of Integrations
The primary obstacle preventing widespread adoption of Jarvis-like AI assistants is the immense difficulty of building comprehensive integrations for all possible services and applications [00:05:15]. Companies like Google or OpenAI are unlikely to build integrations for highly specific services, such as a local city’s park pavilion reservation website [00:05:43]. Users desire a single AI assistant that can augment itself with any capability in the world, rather than managing multiple AI wrappers or applications [00:10:28].
Model Context Protocol (MCP) as the Solution
The Model Context Protocol (MCP) is introduced as a standard mechanism that enables AI assistants to communicate with various tools and services [00:06:33].
Evolution of AI Interactions
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Phase One: ChatGPT and LLM Host Application Layer [00:06:56]
- The release of ChatGPT marked a pivotal moment, not just for the LLM itself, but for the host application layer that provided a good user experience for interfacing with LLMs [00:07:33].
- Initial limitations included manual context provision (copy-pasting text or images) and lack of ability to do anything beyond answering questions [00:07:57].
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Phase Two: Host Application Enables Action [00:08:35]
- The host application began informing the LLM about available services (e.g., search engines, calendar integrations, Slack integrations) to fetch context and perform actions [00:08:41].
- This phase was still limited by the time developers at LLM providers (like OpenAI or Anthropic) could dedicate to building integrations [00:09:24]. Proprietary plugin systems (like OpenAI’s GPT plugin system) create silos, requiring special builds for each platform [00:09:51].
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Phase Three: MCP - The “Do Anything” Era [00:10:55]
- MCP is a standard protocol that all AI assistants will support, allowing developers to build to one specification and be usable by any assistant [00:11:01].
- This is anticipated to bring about a general-purpose Jarvis for everyone [00:11:29].
MCP Architecture
The architecture of MCP involves:
- Host Application: Communicates with the LLM and dynamically manages available services [00:11:42].
- LLM: Knows what services are available and selects the most appropriate tool based on the user’s query [00:12:01].
- MCP Client: A standard client created by the host application for each service, featuring a standard interface [00:12:09].
- MCP Server: Created by the service provider, interfacing with unique tools, resources, prompts, and sampling features [00:12:22].
This standardization of communication between the server and client is what gives AI assistants “hands” to perform actions [00:12:51], enabling enhancing existing systems with AI capabilities.
Demonstration of MCP Capabilities
A demonstration showcased MCP servers integrated with a Cloud Desktop environment, which operates with an LLM [00:13:01]. Key features and capabilities demonstrated include:
- Location Awareness: An MCP server called “locationator” determined the user’s current location [00:13:55].
- Weather Integration: Another server, “get weather,” retrieved current weather conditions for given coordinates [00:14:32].
- Authentication: An “EpicMe” MCP server handled user authentication using OAuth 2.1, making it as secure as other OAuth-based systems [00:14:43].
- Contextual Actions: The LLM, informed by location and weather, could generate and create a journal entry through the authenticated MCP server [00:15:35].
- Dynamic Tagging: The system could check for available tags and create new ones (e.g., “travel” tag for a trip entry) [00:16:12].
- Intelligent Rendering: The LLM could retrieve the journal entry and decide on a user-friendly format (e.g., Markdown) rather than raw JSON, demonstrating the potential for dynamic UI display via future clients [00:16:47].
- Language Translation: The LLM can translate server responses (e.g., English to Japanese) for the user, even if the server only sends responses in one language [00:17:41].
- Full CRUD Operations: The demo included deleting the post and logging out, showcasing full functionality [00:17:53]. Notably, the EpicMe MCP server is designed to be accessible only via MCP clients, not as a traditional web application [00:18:05].
The Future of User Interaction
The transition facilitated by MCP means users will no longer need to rely on browsers or specific search engine phrasing [00:18:15]. Instead, they can naturally speak their questions and intentions, and the AI will understand and execute the desired actions [00:18:42]. This shift moves towards a more natural and direct way of interacting with technology, akin to the vision of Jarvis [00:18:42]. This represents a significant strategy for effective AI implementation and a best practice in future AI development.
Resources
- Model Context Protocol Specification: The core documentation for MCP [00:19:04].
- EpicAI.pro: Kent C. Dodds’s platform for learning about MCP and AI in general, offering posts, workshops, and cohorts focused on the future of user interaction with AI [00:19:09].