Model Context Protocol with Dev Agrawal

Dev Agrawal joins Anthony Campolo to discuss the Model Context Protocol, covering practical implementations and the evolving landscape of AI.

Episode Description

Dev Agrawal joins Anthony Campolo to discuss the Model Context Protocol, covering practical implementations and the evolving landscape of AI.

Episode Summary

In this discussion, the speakers explore how the Model Context Protocol (MCP) standardizes interactions between large language models and a variety of tools, data sources, and prompts. They begin by talking about Dev’s new job, reflecting on advanced engineering practices like event sourcing and domain-driven design. From there, they address how MCP proposes a universal way for AI applications to access and manipulate resources, with analogies to modern web protocols. They also consider the difference between simply coding bespoke integrations versus adopting a shared standard, weighing factors like cost, complexity, and the expanding capabilities of language models. Additional highlights include an overview of tools such as Open Control and Claude desktop, the significance of thoughtful prompt design, and a preview of future projects like personal AI assistants and code review integrations. By the end, they outline how MCP might become a foundational layer for AI-enabled software, bridging everything from file systems and web APIs to higher-level, human-friendly automation.

Chapters

00:00:00 - Introduction and Stream Setup

In this first segment, the hosts greet one another and set the stage for a deep conversation about emerging AI standards. They acknowledge the live streaming platform, briefly noting a technical hiccup where the stream started prematurely, and they welcome viewers who have just joined. The speakers also talk about Dev’s background, sharing quick observations on how the streaming environment can reveal unexpected quirks. This opening establishes a relaxed yet focused tone, inviting listeners to engage with the topics that follow.

They transition into Dev’s new professional role, giving a sense of the advanced engineering practices he’s been tackling, such as domain-driven design and event sourcing. This serves as a perfect lead-in, showing how modern development techniques inform not just the structure of codebases but also how teams collaborate. By highlighting these real-world experiences, the conversation signals that the upcoming discussion on Model Context Protocol is grounded in practical, hands-on knowledge rather than abstract theory.

00:06:00 - A New Job and Specialized Tech

During these minutes, the speakers explore Dev’s move to a fresh engineering position. They discuss the excitement of working with a team already versed in advanced concepts like domain-driven design, describing how this proficiency enables more sophisticated architecture choices. Dev explains how this contrasts with experiences where innovative ideas sometimes get overlooked because of unfamiliarity within a team. The conversation emphasizes the importance of finding the right environment for cutting-edge projects.

They also glance at how event sourcing supports scalable, maintainable applications. This section illustrates how certain strategic patterns can simplify complex software ecosystems, especially when real-time data and distributed processes are involved. The hosts underscore that these techniques are stepping stones for more expansive AI adoption, paving the way for an environment where protocols like MCP can thrive and interoperate with existing solutions, from GraphQL to specialized consulting projects.

00:12:00 - MCP Origins and Early Impressions

Here, the spotlight shifts decisively to the Model Context Protocol. One host recounts discovering MCP around the time Anthropic released initial materials, connecting it to workshops and demos that illuminated the protocol’s distinct features. They note that early chatter on social media sometimes mischaracterized MCP, so they outline the historical and technical context that led to its creation. This background highlights how multiple AI-enthusiast communities collaborated to find common ground.

The speakers point out that MCP isn’t just a way to query or fetch data—it’s designed for entire AI-driven applications, bridging a range of functionalities. They contrast it with well-known protocols like REST or GraphQL, explaining that the intricacies of working with large language models require different core primitives. By the end of this segment, they’ve painted MCP as more than a mere standard for tool access, positioning it instead as a multi-layer approach that handles both data flow and context management.

00:18:00 - Context Windows and Memory Constraints

As the conversation proceeds, the focus turns to the practicality of context within large language model applications. The hosts share anecdotes about pushing the limits of context windows, referencing real projects where code bases, documentation, and user inputs all compete for space in a prompt. They debate the effectiveness of in-context learning, where data is simply appended as text, versus structured retrieval strategies such as vector databases. The cost implications of sending large amounts of tokens are also raised.

They examine the balancing act between immediate convenience and long-term scalability, especially for personal or smaller-scale applications that might comfortably fit everything into a single session. Although vector-based retrieval can optimize token usage, expanding context windows may negate the need for complex orchestration in many scenarios. By weighing these trade-offs, they highlight the evolving nature of best practices in AI development, showing how each approach can be situationally advantageous.

00:24:00 - MCP Resources and Prompts

In this section, the hosts walk through the concept of “resources” in MCP, contrasting it with standard RESTful thinking. They describe resources as more flexible entities—files, log data, or any form of context—that can be served to the language model. Detailed metadata like name and MIME type carries significance since large language models benefit from descriptive clues about content. Moreover, the discussion touches on how prompts fit into MCP as a structured mechanism rather than a simple text blob.

They underscore how crucial it is that resources do not merely exist for the model’s consumption but also for user-facing interfaces and tools. The ability to list and reference them systematically offers transparency and control, which can be critical in auditing and refining AI interactions. By illustrating how prompts can embed specialized instructions, they explain how MCP ensures that these instructions become integral to the protocol, supporting both straightforward queries and complex multi-step workflows.

00:30:00 - Introducing Tools and Business Logic

Building on the idea of resources, the hosts introduce “tools” within MCP. They share a simple example: a tool that adds two numbers. Through that demonstration, they clarify how tools encapsulate business logic or external API calls that a model can invoke. They note that these aren’t just endpoints; descriptions must be meaningful so models can autonomously figure out when, and how, to employ each tool.

Further, they connect tools to real-world use cases like weather information or database queries, emphasizing that each tool can come with constraints or explicit parameters enforced by schemas. They relate this concept to the rising popularity of function calls in AI frameworks, explaining that MCP is effectively universalizing these patterns. By detailing how a function schema can guide a model’s inputs and outputs, they show how the protocol mitigates hallucinations and shapes more coherent interactions.

00:36:00 - Logging, SSE Transport, and Early Challenges

Around this point, the speakers address some of the rough edges in the TypeScript SDK for MCP. Attempting to set up features like logging and Server-Sent Events (SSE) reveals that parts of the documentation remain underdeveloped. They chuckle about encountering methods or parameters that no longer match the official guidelines, a sign of how fast the ecosystem evolves. Despite these hiccups, they remain optimistic, comparing it to the early stages of other influential protocols.

They also touch on the synergy between MCP and major LLM services. They mention how anthropic’s approach and openAI’s own developments converge, noting the initial reluctance that can exist when a leading company adopts a competitor’s innovation. The emergent takeaway is that growing pains are natural in cutting-edge tech. The promise of unifying AI services and tooling under a consistent protocol outweighs the short-term headaches of mismatched versions and swiftly evolving documentation.

00:42:00 - Cost Factors and Model Switching

Here, conversation circles back to token economics, exploring when it’s preferable to pass entire codebases to a model versus employing vector-based retrieval. As context windows enlarge, some see a future where even hefty repositories can be fed in directly, potentially bypassing complicated retrieval pipelines. Others remain cautious about mounting expenses and the complexities of real-world enterprise usage. The result is a balanced discussion of the interplay between practical resource limits and user requirements.

They then pivot to the notion of switching between different LLMs seamlessly. The participants highlight how standardizing around MCP might enable dynamic model selection, where smaller, cheaper models handle routine tasks and more powerful models handle complex reasoning. This potential scenario reflects the protocol’s broader vision: a modular environment where application needs and budget constraints guide how data is processed and where.

00:48:00 - Implementing MCP in Practice

In this portion, the speakers recount personal experiments with creating servers and hooking them into local or hosted environments. They talk about the significance of a single standardized approach to handle diverse tasks: from listing resources to orchestrating tools, everything flows through the same protocol. The conversation revisits how prompts become first-class citizens in this architecture, no longer treated as afterthoughts but integrated pieces that influence AI outcomes.

They also address the concept of an “MCP host,” explaining how multiple clients can run side by side, or how a single application might unify them. While acknowledging that it sounds technical, they provide real-world parallels, comparing it to the well-known patterns in microservices or GraphQL federation. Ultimately, the aim is for developers to have a stable, proven scaffold for AI workflows without reinventing the same solutions every time a new AI capability emerges.

00:54:00 - Open Control and Ecosystem Synergy

Turning to Open Control—a project championed by Dax and the SST team—the hosts illustrate how a robust framework can streamline early-stage hurdles. They mention how Open Control includes a built-in client interface so developers can quickly see how an MCP server responds without sending raw JSON. This kind of improved developer experience bridges the gap between protocol theory and tangible building blocks.

They also compare Open Control’s approach to other emerging projects, emphasizing that an ecosystem is forming around MCP. Because the protocol unifies so many aspects—tools, resources, prompts—libraries are popping up to simplify each piece. The hosts predict that once these scaffolds mature, using MCP could be as routine as spinning up a web server today. This broader acceptance would, in turn, bring more standardization and stronger community support.

01:00:00 - Reflecting on AI Development and Redwood

In this interval, they briefly shift to the RedwoodJS community, referencing prior conversations and potential new directions for Redwood’s future. The speakers muse about Redwood’s structure, how it might adapt in a world increasingly influenced by AI tools and protocols. They see parallels between Redwood’s microservice-like layout and MCP’s resource-centric design, hinting that synergy could arise for Redwood-based AI applications.

The discussion also examines the role of frameworks in bridging front-end and back-end concerns. They argue that Redwood’s approach, with its emphasis on developer-friendly conventions, might integrate neatly with the streamlined nature of MCP. Despite Redwood’s original focus on Jamstack, the conversation posits that modern AI features require more fluid data flows, which Redwood can address as it continues evolving.

01:06:00 - Building Personal AI Assistants

At this point, Dev shifts gears to outline a personal side project: an AI assistant that could interface with everyday tasks. He envisions hooking it into emails, Slack, calendars, and even more personal domains like journaling, thereby creating a single hub of intelligence. The ambition is to have the assistant become a capable collaborator, scheduling meetings, drafting messages, or updating project boards based on natural language directives.

They reflect on the complexities of granting AI broad access to personal data. Questions around asynchronous tool invocation—where a model queues up a series of actions rather than immediately requesting them—highlight the potential for more advanced planning. By painting a picture of a meeting scenario where tasks are recorded in real time, the hosts underline a future where AI is less about reactive chat and more about proactive collaboration, leveraging protocols like MCP to integrate it all seamlessly.

01:12:00 - Practical Security and Privacy Concerns

Security and privacy surface as major considerations in an AI-driven world. The conversation acknowledges that while many are used to home assistants or phone microphones, a wearable or always-listening device raises new dimensions of data exposure. They note that major platform constraints—like Apple’s watchOS policies—can also limit continuous data capture, meaning developers face both ethical and technical hurdles.

They then broaden the lens, suggesting that trust models may evolve as AI becomes more ubiquitous. People have grown somewhat accustomed to devices collecting data in the background, but official acceptance of an always-listening personal AI might be a bigger leap. Even so, the hosts highlight that the potential upside—automated note-taking, real-time organization—could outweigh these anxieties if implemented transparently, with robust user controls.

01:18:00 - Projects, Open Sourcing, and Community Collaboration

In this segment, they stress that in a fast-moving AI space, open sourcing ideas often leads to richer innovation. The speakers mention examples of tools or frameworks where proprietary approaches quickly gave way to open collaboration—MCP being a prime illustration of a collective standard that many big players now support. They reason that the breakneck pace of AI progress makes it nearly impossible to protect exclusive concepts before the community replicates or surpasses them.

This reasoning supports a strategy of synergy and shared progress, where developers pool efforts rather than continuously competing in walled-off silos. The speakers also describe personal experiences of seeing near-identical projects emerge, underlining that it’s usually better to team up than to splinter the ecosystem. It echoes the ethos of open source communities everywhere: momentum arises when knowledge is freely exchanged and built upon.

01:24:00 - Future Streams and Upcoming Plans

As the conversation begins to wind down, the hosts outline potential topics for future sessions. They entertain diving deeper into MCP, possibly exploring specialized frameworks like Open Control or even branching into Redwood’s next developments. The mention of personal code review agents and AI-driven meeting bots also sparks excitement, illustrating a broad frontier for further experimentation.

They reiterate the practicalities of scheduling and traveling to conferences, noting Dev’s upcoming speaking engagements. Between Star Trek-themed gatherings and local-first conferences in Berlin, they joke about balancing real-life events with the continuous wave of AI announcements. These reflections encapsulate the essence of the session: a blend of day-to-day hustle and long-term ambitions, all framed by an enthusiasm for AI’s ever-expanding potential.

01:28:56 - Closing Thoughts and Farewell

In this final portion, they officially wrap up, thanking viewers for tuning in and referencing a quick raid for the few people watching live on Twitch. They share words of encouragement for anyone eager to experiment with AI technology, acknowledging that this moment in tech history is brimming with opportunity. The mood is hopeful, pairing a sense of possibility with a realistic nod to the challenges ahead.

By signing off in a casual, friendly manner, the hosts reinforce that they see these conversations as ongoing. MCP and related AI innovations are still in their infancy, so they invite fellow developers to keep building, learning, and sharing. Their enthusiastic farewell sets the stage for future episodes—where deeper integrations, new protocols, and broader adoption of AI tools will undoubtedly continue to reshape software development. This marks the conclusion at 01:28:56, signaling the end of the discussion and the total duration of the session.