We’ve all been there: the rush to get AI integrated, often leading to the simplest path – slapping an MCP layer on top of existing APIs. It felt like progress, a quick win. But as we collectively gain experience, a more nuanced, and frankly, more challenging truth is emerging: most of these initial MCP servers are built ‘wrong.’ It’s not a criticism of effort, but rather a maturation of our understanding about what an AI-native system truly demands.
Strategic Analysis
The core issue isn’t just about exposing data; it’s about exposing usable, context-aware data tailored for an LLM’s unique cognitive architecture. Our traditional APIs were designed for human developers or other software systems, assuming a level of interpretation, memory, and error recovery that LLMs simply don’t possess without explicit guidance. Throwing a raw database schema or an unfiltered API response at an LLM is like handing a chef a grocery store full of ingredients and no recipe – it’s access, but not actionable access within the constraints of their ‘context window’ and processing style. This is why we’re seeing a critical shift from simple wrappers to ‘LLM-first’ server design, where filtering, summarizing, and presenting only relevant information becomes paramount.
This new paradigm embraces the LLM as a distinct kind of user, one that thrives on structured input, clear tool definitions, and actionable error messages. Imagine an MCP server that doesn’t just tell an LLM ‘you can query this database,’ but actively guides it: ‘Here are the tables most relevant to your current query, and here’s how to construct a context-sensitive search.’ It’s about building in a ‘state machine’ enforced by the API itself, guiding the LLM through a logical flow and providing self-correction instructions when things go awry. We’re even seeing developers iterate on their MCP designs by asking the LLM itself how to improve usability and tool documentation, a truly meta approach to AI UX.
Crucially, the evolution of the MCP specification itself is enabling this shift. The move to streamable web protocols, replacing janky server-sent events with proper bi-directional streaming, is a game-changer. This isn’t just about efficiency; it’s about enabling real-time, dynamic interactions that are essential for complex agentic workflows. Combine this with structured tool output, which ensures AI responses are organized and predictable, and you start to see the scaffolding for truly sophisticated AI agents. Tools like Supergateway, emerging from the community, are already bridging the gap, converting older STDIO servers to these modern, streamable formats, highlighting the pragmatic steps being taken to accelerate adoption.
Ultimately, this isn’t just an API evolution; it’s a fundamental re-imagining of application architecture. We’re moving beyond thin-skinned clients for human applications and building an entirely new class of server designed explicitly for intelligent, function-calling, multimodal inference engines. While the goals of human and AI applications might often align, the underlying design principles and interaction patterns are vastly different. This realization is pushing us to treat the LLM not as an afterthought, but as the primary consumer for whom the server is meticulously crafted.
Business Implications
For developers and leaders, the message is clear: stop thinking of MCP as just another API endpoint. Start designing your services with an LLM in mind from the ground up. This means prioritizing context management – how do you slice, filter, and summarize data for optimal consumption? Embrace streamable protocols for more fluid and efficient interactions. Pay obsessive attention to ‘AI UX’ – how can your tools and their outputs be structured to minimize LLM hallucinations and maximize effective tool use? And don’t be afraid to iterate on your designs by putting an LLM through its paces, asking it how to improve the interface.
Future Outlook
We’re still in the early innings of MCP maturity, and the current ‘wrapper’ phase was a necessary stepping stone. But the trajectory is undeniable: we’re moving towards increasingly sophisticated, LLM-native server designs. This will unlock multi-LLM agentic workflows, where specialized AIs collaborate seamlessly, and enable a new generation of applications where AI is not just a feature, but the core interaction paradigm. Expect to see more tooling emerge to help bridge legacy systems, and a growing emphasis on security and elicitation patterns as these intelligent agents gain more capabilities and autonomy. The future of AI-driven systems hinges on our ability to build not just with LLMs, but for them.
Sources & Further Reading
- Most MCP servers are built wrong - r/mcp
- What is stopping us from incorporating MCP in our day to day work? - r/mcp
- Streameable HTTP server wrapper around STDIO MCP server - r/mcp
- Supergateway v3.2 - streamable HTTP from stdio - r/mcp
- Claude Code Gains Support for Remote MCP Servers over Streamable HTTP - infoq.com - Google News MCP