The AI Protocol That Forgot Four Decades of Wisdom

Hello everyone. Strap in, because today we’re talking about the Model Context Protocol – or MCP – which has been triumphantly paraded as the “USB-C for AI.” And before you roll your eyes any further back than mine are right now, understand this: we’ve seen this movie before. Except this time, instead of a predictable popcorn flick, enterprises are betting millions on a plot that’s missing the last four decades’ worth of production notes from the distributed systems genre. What could possibly go wrong? Oh, only everything.

The Dream vs. The Waking Nightmare

MCP’s pitch is “simple integration for AI tools.” Great – if you’re running a weekend hackathon or some toy sample web app. Not so great when it’s sitting at the heart of a financial trading system, hospital diagnostic tool, or manufacturing quality control process. It’s as if someone decided, “Hey, let’s do air traffic control using Slack emojis – it’ll be fun!” Then they’re shocked when 200 planes don’t land where they’re supposed to.

The AI bubble has pumped MCP adoption into hyperspeed. Enterprises are rolling it out not because the protocol meets their needs, but because no one wants to miss the gold rush. It’s all sizzle, no steak – except the steak turns out to be your production environment on fire.

The Hall of Forgotten Lessons

History is a teacher, but MCP clearly skipped class. UNIX RPC in 1982 gave us External Data Representation (XDR) and Interface Definition Language (IDL) for predictable type safety across systems. MCP tossed that in the bin for schemaless JSON and “optional hints,” meaning your AI can interpret a timestamp like a fortune cookie message and nobody stops it until your bank’s trading bot starts slicing decimal points like you slice bread.

CORBA in ‘91 knew different languages needed consistent bindings so a Java client wouldn’t faint when a C++ server throws. MCP? Nah – every language implementer for themselves. Python and JavaScript are guaranteed to interpret certain values differently, especially floats. What could possibly break when entire platforms can’t agree on numbers? Oh, just everything involving science, math, finance, or reality itself.

Remember how REST brought statelessness for scaling? Or how SOAP brought machine-readable contracts and integrated security? MCP cherry-picks the least functional ideas from these approaches, staples them together like a Frankenstein that forgot a brain, then leaves you to deal with ad hoc retries, session stickiness, and schema roulette.

The Extension Swamp

Point out an MCP flaw and the defenders start handing you a buffet of third-party “fix” libraries, each with wildly varying quality. Need auth? There’s a wrapper for that. Need tracing? Oh, try this extension… or maybe that one. This is how you go from “a standard protocol” to “a grim Pokémon hunt of unmaintained GitHub projects.”

• Which library is the “official” one? None – pick your poison.
• Will it be supported in two years? Flip a coin.
• Do they interoperate? The Magic 8-Ball says: doubtful.

Enterprise architecture is about convergence, not scattering your stack across a minefield of hobby projects. gRPC, REST, SOAP – every serious protocol worth its salt built core enterprise features in from the start. MCP leaves you to duct-tape them on and pretend it’s normal.

Patch Notes as Admissions of Guilt

The 2025-03-26 spec update reads like a laundry list of “Oops, we forgot that” – OAuth, session management, progress updates. These aren’t features, they’re firefighting measures retrofitted in after people got burned. It’s like surgically implanting a parachute after you jumped out of the plane.

Operational Gaps You Can Drive a Truck Through

• No distributed tracing – so debugging feels like rummaging through a dumpster for a receipt.
• No cost attribution – good luck explaining that $50k AI bill to finance.
• No service discovery – your multi-region dreams die in DNS hell.
• No schema versioning – upgrade roulette with every deployment.
• Performance bottlenecks – still using stdio transport like it’s 1979.

For demos, sure, it’s “good enough.” The same way my blood pressure is “good enough” after three espressos – right before the heart monitor starts wailing.

The Stakes Are Real, and So Are the Consequences

AI is no longer a novelty – it’s embedded in finance, healthcare, manufacturing, and customer service. These domains are allergic to catastrophic failure. MCP’s “move fast, break everything” attitude is less “innovative disruption” and more “systemic negligence.” We figured out robust distributed systems decades ago; those lessons aren’t optional, they’re survival kits.

The Final Prescription

As your friendly digital doctor, my professional diagnosis is that MCP suffers from chronic feature deficiency, acute security negligence, and an advanced case of magical thinking. Treatment involves immediate infusion of real, enterprise-grade features – type safety, service discovery, binary protocols, proper error taxonomies – and a strict diet free of hype-driven decision-making.

Until then, enterprises are basically beta-testing someone else’s homework, and the only guaranteed output is operational debt. Good idea for lab experiments? Maybe. Safe for production AI tooling? As safe as letting an unpatched Windows 98 box guard the Pentagon’s firewall.

My prognosis: bad, unless MCP’s maintainers start playing the long game instead of this demo-driven feature chase. For now, I’d advise enterprises to keep their wallets and production environments locked up tight. And that, ladies and gentlemen, is entirely my opinion.

Source: MCP overlooks hard-won lessons from distributed systems, https://julsimon.medium.com/why-mcps-disregard-for-40-years-of-rpc-best-practices-will-burn-enterprises-8ef85ce5bc9b