The buzz around Model Context Protocol (MCP) is undeniable, as recent community threads reveal a palpable excitement about empowering AI agents to interact with virtually any system. From turning CLI tools into discoverable assets with YAML, as seen with Enact Protocol, to discussions about generating custom MCP servers for private APIs, the prevailing sentiment is one of boundless possibility. But what if, in our haste to connect everything, we’re inadvertently recreating the very integration challenges MCP is uniquely positioned to solve?
Strategic Analysis
It’s easy to get swept up in the immediate utility of MCP as a flexible middleware, a ‘body for AI’ as one discussion highlighted. The impulse to build custom MCP servers—whether manually with Express.js or using generators from Postman—to bridge private APIs with LLMs is a natural first step. This approach, while quickly unlocking AI’s potential for specific tasks, echoes the early days of API integrations where every connection was a bespoke, hand-coded affair. Developers spend time wrestling with ‘sample bodies, headers, and responses,’ creating tightly coupled, often brittle, wrappers for each unique interaction.
However, this focus on bespoke wrapping risks reintroducing the ‘integration spaghetti’ that modern API management sought to overcome. Just as an API Gateway evolved beyond simple routing to offer standardized security, rate limiting, and discovery, MCP’s true strength isn’t in enabling any custom integration, but in fostering standardized and reusable tool definitions. The current enthusiasm, while productive in the short term, often overlooks the long-term maintenance burden and lack of discoverability inherent in a fragmented ecosystem of custom-built adapters.
The real breakthrough lies in initiatives like Enact Protocol and HAL (HTTP API Layer). These aren’t just more ways to build custom wrappers; they represent a fundamental shift from ‘coding integrations’ to ‘defining tools.’ Enact’s YAML definitions allow CLI tools to become dynamically discoverable and executable by AI agents, not through custom server code, but through a standardized metadata layer. Similarly, HAL’s ability to auto-generate tools directly from OpenAPI/Swagger specifications for any HTTP endpoint moves beyond bespoke coding into a realm of protocol-driven integration. This elevates MCP from a mere ‘API to LLM tool serving’ into a robust framework for declarative, reusable AI capabilities.
This standardization promotes genuine reusability, allowing tools to be shared and discovered across different AI agents and applications, rather than being confined to the specific custom wrapper they were built into. It enables AI agents to ‘search for tools’ and ‘register them dynamically at runtime’ based on well-defined, machine-readable specifications, reducing the boilerplate and cognitive load for developers. The challenge, as one comment on HAL noted, is ensuring these auto-generated tools have ‘strong tool prompting’ and align with end-user language, pushing the complexity from integration code to tool definition quality.
Business Implications
For developers and businesses, this alternative perspective suggests a strategic pivot. Instead of allocating resources to crafting and maintaining a growing fleet of custom MCP wrappers, the focus should shift towards investing in robust tool definition languages, comprehensive API specifications (like OpenAPI), and platforms that can translate these into standardized MCP tools. This means less time building one-off integrations and more time curating a rich, discoverable library of AI-ready capabilities, fundamentally altering the architecture of future AI applications towards composability and abstraction.
Future Outlook
If this shift towards standardized tool definition prevails, the future of AI application development will look less like a collection of isolated custom agents and more like a dynamic ecosystem of interoperable, discoverable tools. We could foresee a vibrant marketplace of ‘AI capabilities’ where agents dynamically compose complex workflows from a shared, well-defined pool of functions, much like microservices interact through standardized APIs. This move positions MCP not just as an API gateway for AI, but as the foundational layer for a truly composable and intelligent software landscape, where the emphasis moves from how to integrate to what capabilities are available.
Sources & Further Reading
- Enact Protocol - Turn any CLI tool into a discoverable MCP tool - r/mcp
- Generate a mcp server - r/mcp
- I think im understanding MCPs in the wrong way…. - r/mcp
- I made an MCP server that gives LLMs a full HTTP toolkit - r/mcp
- HAL (HTTP API Layer) – HAL (HTTP API Layer) is a Model Context Protocol (MCP) server that provides HTTP API capabilities to Large Language Models. - r/mcp