Why MCP’s Disregard for 40 Years of RPC Best Practices Will Burn Enterprises
Fool me once, shame on you; fool me twice, shame on me. Julien Simon 9 min read · Jul 29, 2025 -- 26 Listen Share
The Model Context Protocol (MCP) promises to standardize AI-tool interactions as the “USB-C for AI.” While its simplicity accelerates adoption, MCP systematically overlooks four decades of hard-won lessons from distributed systems. This isn’t an academic concern: enterprises deploying MCP today are building on foundations that lack fundamental capabilities that every production remote-procedure calling (RPC) system, since 1982, has deemed essential.
The Dangerous Gap Between Hype and Reality
MCP advocates position the protocol as production-ready infrastructure, but its design philosophy, prioritizing ease of adoption over operational robustness, creates a ticking time bomb for enterprises. The same simplicity that enables a developer to integrate a tool in an afternoon becomes a liability when that tool handles millions of requests with real business impact.
The AI hype cycle has accelerated adoption beyond the architectural maturity of MCP. Companies are deploying MCP not because it meets their operational requirements, but because the AI gold rush demands immediate action. This mismatch between expectations and capabilities will lead to painful production failures.
Four Decades of Lessons Ignored
Let’s start with UNIX RPC, introduced in 1982. The creators understood something fundamental: when systems speak different languages or run on heterogeneous architectures, you need more than good intentions to ensure a 32-bit integer on one system doesn’t become garbage data on another. Their solution, External Data Representation (XDR), wasn’t over-engineering. It was essential for systems where data corruption could result in system failure. The Interface Definition Language (IDL) with compiler-generated stubs caught type mismatches at build time, not runtime.
MCP discards this lesson, opting for schemaless JSON with optional, non-enforced hints. Type validation happens at runtime, if at all. When an AI tool expects an ISO-8601 timestamp but receives a Unix epoch, the model might hallucinate dates rather than failing cleanly. In financial services, this means a trading AI could misinterpret numerical types and execute trades with the wrong decimal precision. In healthcare, patient data types get coerced incorrectly, potentially leading to wrong medication dosing recommendations. Manufacturing systems lose sensor reading precision during JSON serialization, leading to quality control failures.
CORBA emerged in 1991 with another crucial insight: in heterogeneous environments, you can’t just “implement the protocol” in each language and hope for the best. The OMG IDL generated consistent bindings across C++, Java, Python, and more, ensuring that a C++ exception thrown by a server was properly caught and handled by a Java client. The generated bindings guaranteed that all languages saw identical interfaces, preventing subtle serialization differences.
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