The recent discussions across forums, like the one on r/mcp, clearly show a palpable tension: Is the Model Context Protocol destined to be little more than a sophisticated tool-calling mechanism, or will it blossom into the full ‘context-aware AI’ standard its proponents envision? There’s a strong current of optimism that MCP, like HTTP, will evolve beyond its current perceived limitations. But what if this ‘limitation’ isn’t a bug in development, but a feature of practical adoption—a natural gravitation towards its most impactful and implementable role?
Strategic Analysis
The prevailing sentiment, particularly among developers, suggests a struggle to realize MCP’s broader ambitions beyond remote tool execution. As highlighted in the community, features like ‘elicitation’ or ‘progress tracking’ often lack robust client-side support, leading to developer frustration. This isn’t merely a sign of ‘lack of maturity’ as some suggest; it’s a market signal. If the ecosystem isn’t investing in client implementations for these richer contextual features, it begs the question of whether the demand for them, or their practical utility within the protocol itself, is truly compelling enough to justify the overhead.
Comparing MCP’s trajectory to HTTP’s evolution, while aspirational, might be a misdirection. HTTP’s genius lies in its simplicity and universality for data exchange, allowing complex applications to be built on top of it. It didn’t try to be a ‘full-stack’ application framework. Similarly, the ‘stateless MCP’ RFC, which prioritizes scalable tool calling, isn’t a retreat from vision but a pragmatic acknowledgment of what developers need and can reliably build today. Trying to force a protocol to encompass every facet of agentic behavior—from ‘thinking’ to ‘progress’ to ‘resources’—risks creating an overly complex, partially implemented standard that struggles with adoption.
Other emerging agentic protocols, like the ones discussed in the context of ‘Sequential Thinking’ vs. ‘Cline,’ often focus on specific layers of the AI stack—be it task orchestration, planning, or direct interaction. MCP’s strength, therefore, might not lie in trying to do everything those specialized protocols do, but rather in providing a foundational, standardized communication layer for AI agents to reliably execute actions and exchange critical, minimal context.
This isn’t to say that ‘context’ or ‘elicitation’ aren’t important for AI; they absolutely are. However, the contrarian view is that these complex cognitive functions might be better handled by the agents themselves or by specialized services that utilize a focused MCP for their communication, rather than attempting to embed every nuance into the protocol’s core. The practical reality of building and maintaining robust clients and servers suggests that a narrower, well-defined scope is often the path to widespread utility.
Business Implications
For developers, this means shifting focus from lamenting MCP’s perceived ‘narrowing’ to embracing its potential as a highly optimized, standardized ‘AI RPC’ layer. Instead of trying to bend MCP to every theoretical agentic need, developers should leverage its strengths for reliable tool execution and efficient context exchange, building more complex agentic logic and state management above the protocol. For businesses, this translates to a clearer value proposition: MCP as the backbone for integrating AI with existing systems through robust, discoverable tooling, rather than a magic bullet for end-to-end autonomous intelligence. Leaders should prioritize robust integrations over chasing a protocol that promises to do everything.
Future Outlook
If MCP leans into its role as the industry standard for AI tool orchestration and essential context sharing, it has the potential to become an invisible, ubiquitous layer, much like foundational internet protocols. Its long-term evolution may not be about expanding the feature set dramatically within the protocol itself, but about becoming the highly reliable conduit through which specialized AI agents and services interact. The true battleground for ‘smarter, context-aware AI’ might move to the application layer built on top of MCP, where complex reasoning, memory, and multi-modal interactions are orchestrated, leaving MCP to do what it increasingly does best: enable AI to act and react reliably in the digital world.
Sources & Further Reading
- Is MCP being resumed to a “tools server”? - r/mcp
- Does Sequential thinking do anything different than cline? - r/cline
- What Is Model Context Protocol (MCP)? A New Standard for Smarter, Context-Aware AI - Precisely - Google News Model Context Protocol
- MCP vs A2A: Agentic AI protocols take shape - theregister.com - Google News Model Context Protocol