For the first time in years, developers are saying the work feels fun again.
That’s not just sentiment. It’s the result of how dramatically AI-native workflows have reshaped the development experience. With AI code editing tools like Cursor, large language models (LLMs) are no longer external assistants; they’re embedded into the integrated development environment (IDE), working alongside developers to reduce friction, eliminate repetitive patterns and keep context where it belongs: in the editor.
This approach, widely referred to as vibe coding, prioritizes creative flow over boilerplate. It’s not about producing more lines of code. Instead, it’s about spending less time on the ones no one wants to write. And that has a real impact on how developers work, collaborate and feel about their craft.
In the past few months, vibe coding has gone from curiosity to core practice. But even as it improves the software development experience, it still hits a hard wall.
MCP: The Missing Link Between Code and Infrastructure
Application development, even in AI-first workflows, remains disconnected from infrastructure. While your AI assistant can generate a Terraform block or debug a snippet, it has no understanding of what’s deployed, what’s drifting or what already exists. Identity and access management (IAM) configurations, orphaned services, Kubernetes state — this context all lives in other tools, out of reach for the LLM.
This disconnect limits what’s possible. AI tools may be smarter than ever, but they’re still operating without visibility. That’s what the next evolution solves.
A new generation of tools built around the Model Context Protocol (MCP), an open source standard introduced by Anthropic, is changing the game — bridging code and infrastructure, and giving agents real access to real context.
MCP allows AI systems to interact with structured data sources, tools and even other agents in a unified, predictable way. Think of it as the USB-C for LLMs: a common interface that lets AI-native applications connect to real-world systems without complex and custom integrations.
This approach is quickly becoming the default.
In practice, this means any MCP-compliant product instantly becomes accessible to a growing ecosystem of LLM-powered agents, copilots and developer workflows. And that opens the door to a wide array of use cases, from real-time infrastructure-aware code generation in the IDE to fully automated remediation across cloud environments.
The Developer Productivity Angle: More Than Speed
This shift has real implications for developer productivity, not just in terms of speed, but how developers work.
In traditional dev workflows, accessing cloud state requires searching, switching tools and interpreting semi-structured data across systems. It’s slow, error-prone and hard to scale. Integration into tools like Cursor via MCP changes that. When an LLM agent can pull accurate, live data from your cloud stack in context — in the middle of a coding session — it eliminates the guesswork and shortens the feedback loop dramatically.
More importantly, it shifts the role of developers from investigators to designers. Instead of spending time figuring out what exists, what broke or what drifted, developers can focus on higher-order work — solving problems, optimizing systems and shipping features with confidence that their AI systems have their back.
And the impact is already visible. Early adopters like ZoomInfo and Hewlett Packard Enterprise (HPE) are leveraging Firefly’s MCP capabilities not just for app development, but to accelerate platform engineering workflows — enabling Infrastructure as Code (IaC) automation that’s responsive to real-time cloud state.
Unlocking AI-Native Cloud Operations With MCP Servers
The promise of AI-native development isn’t just faster iteration or cleaner autocomplete; it’s about enabling systems that can reason over complex environments, prioritize intelligently and take meaningful action. But that promise hinges on one thing: access to real data.
Now is the time to harness AI where it excels, by exposing it to the kinds of rich, structured and dynamic data sets that it can parse, summarize, synthesize and build upon. This isn’t limited to code generation or log parsing. It extends to the infrastructure running our systems: policies, permissions, workloads, service topologies and every signal embedded across the cloud.
That’s where MCP servers, like Firefly’s, come in.
Here’s an example: Firefly already maintains a deeply indexed, real-time inventory of cloud environments — such as Kubernetes workloads, IAM configurations and GitHub integrations. Until now, this context has been accessible via dashboards or APIs. But in an agent-driven world, that isn’t enough. So the Firefly MCP Server exposes this data, making it available to any AI-native tool or agent. It turns your infrastructure into a live, queryable interface — available inside your IDE, inside automation platforms like n8n or inside custom LLM agents built for your organization.
The rise of MCP servers like these marks a shift from static integration to dynamic interoperability. It’s no longer about pushing data between disconnected systems. It’s about enabling agents to operate on shared truth, in real time.
And once that’s possible, the use cases expand rapidly:
- Infrastructure-aware copilots that generate code based on your actual cloud state.
- Design-time validation against production configurations.
- Automated drift detection and remediation triggered by agents
- Self-healing systems powered by live inventory and policy logic.
- Proactive cost and performance optimization using usage-aware context.
This isn’t just about better integrations. It’s about unlocking the next layer of AI-native development: where cloud context is not an afterthought but a foundational input to every decision an agent or developer makes.
And this kind of innovation is what turns the cloud from a black box into a usable, intelligent interface and opens the door to a new class of smarter systems built with infrastructure in the loop from day one.
But that’s just the surface.
MCP servers like Firefly’s are poised to become the gateway between the cloud and the rapidly expanding agentic ecosystem. Every LLM agent with an MCP client can now interact with your infrastructure — not by guessing but by asking. And once that interface exists, the use cases multiply: IaC grounded in reality, agent-led incident remediation, self-healing systems, proactive cost optimizations and design-time architecture validation.
It’s not just about building better apps. It’s about building smarter systems where application logic, infrastructure state and AI reasoning are no longer isolated domains.
In this world, cloud data isn’t a backend detail; it’s a first-class input.
A New Layer in the AI Stack
The growing momentum around MCPs reflects a broader realization: AI agents are only as useful as the data and context they have access to. As more companies adopt agent-first tooling, the bottleneck shifts from generation to grounding. How easily can an agent understand your environment?
Firefly's MCP Server answers that challenge head on. It turns your existing cloud inventory into an accessible interface for any AI-native system. Whether you’re building a custom copilot, integrating automation workflows or enabling AI-assisted incident response, Firefly makes your infrastructure data not just visible but usable.
This unlocks a model where every AI agent can be cloud-aware — not by reinventing your environment but by plugging into a shared source of truth. It’s an evolution in how developers interact with infrastructure: faster, more contextual and more intuitive.
As the ecosystem matures, we’ll likely see a growing set of MCP-powered integrations that stitch together cloud, code, collaboration and automation into cohesive developer experiences.
In short: This is the new interface to your cloud.
And it’s already making developers not just faster but more focused, more empowered and — ultimately — happier.
To dive deeper into how our MCP server works, explore the documentation.
