The Protocol That Forgot History
This headline isn’t actually about protocols. It’s about what happens when an industry forgets its own lessons.
It’s 3 AM on a Tuesday. Sarah, a senior engineer at a mid-sized fintech company, is staring at her laptop in a coffee shop because her home internet went down an hour ago. The production incident started at 2:17 AM: their AI-powered fraud detection system is making inexplicable decisions. Legitimate transactions are being flagged. Suspicious ones are sailing through. The customer support queue is exploding.
She’s been grepping through JSON logs for forty minutes, trying to trace a single AI decision through twenty different tool calls across five services. Each service has its own log format. There are no correlation IDs. No trace contexts. Just fragments of JSON scattered across systems like breadcrumbs in a forest after rain.
Her phone buzzes. It’s her manager: “Any update? We’re bleeding customers.”
Sarah knows this could take days to debug. She also knows it didn’t have to be this way.
Six months ago, when her team adopted the Model Context Protocol (MCP) to connect their AI agents to internal tools, it seemed like the perfect solution. “The USB-C for AI,” the documentation promised. Simple. Elegant. Production-ready.
The integration took an afternoon. The demo wowed executives. The rollout was fast.
But nobody asked the question that matters: Has anyone done this before?
The Pattern We Keep Missing
Here’s what’s fascinating about technology: we keep having the same conversation, just with different acronyms.
In 1982, engineers at Sun Microsystems faced a problem: how do you make computers that speak different languages talk to each other reliably? They created UNIX RPC with External Data Representation (XDR) because they learned something painful: when a 32-bit integer on one system becomes garbage data on another, people lose money. Systems fail. The solution wasn’t elegant, but it worked.
In 1991, CORBA emerged from similar pain. In heterogeneous environments—Java talking to C++, Python to Ada—you can’t just “implement the protocol” in each language and hope for consistency. The OMG Interface Definition Language generated identical bindings across every language because subtle serialization differences weren’t theoretical problems. They were production disasters.
By 2000, REST taught us that statelessness enables horizontal scaling. SOAP, despite its verbosity, showed us that machine-readable contracts matter when you’re building systems that need to work together for years, not days.
In 2016, gRPC demonstrated why observability isn’t optional. Built-in distributed tracing wasn’t a nice-to-have feature. It was the difference between debugging a production issue in 30 minutes versus 3 days.
Each of these protocols learned from the failures of what came before. Each incorporated hard-won lessons about what breaks in production.
MCP ignores all of it.
When Simplicity Becomes Dangerous
The Model Context Protocol makes a bet: that simplicity in adoption matters more than robustness in operation. For demos and experiments, this bet pays off brilliantly. A developer can integrate an AI tool in an afternoon. The code is clean. The concepts are straightforward.
But here’s what happens when that same simplicity hits production:
The Type Safety Trap: MCP uses schemaless JSON with optional, non-enforced type 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 doesn’t fail cleanly—it hallucinates dates.
In Sarah’s case, this meant a trading AI misinterpreting numerical types and executing trades with the wrong decimal precision. In healthcare, it means patient data types getting coerced incorrectly, potentially leading to wrong medication dosing recommendations. In manufacturing, it means sensor readings losing precision during JSON serialization, leading to quality control failures.
These aren’t theoretical concerns. These are the exact problems that UNIX RPC solved in 1982 with XDR.
The Language Lottery: Each language implements MCP independently. Python’s JSON encoder handles Unicode differently than JavaScript’s. Float representation varies. Error propagation is ad hoc.
When Sarah’s frontend JavaScript and backend Python interpreted MCP messages differently, she got integration nightmares that only surfaced under load. Third-party tools using different MCP libraries exhibited subtle incompatibilities only in edge cases—the kind that happen at 2 AM on production systems.
CORBA solved this in 1991 with generated bindings that guaranteed consistency. MCP asks us to rediscover why that mattered.
The Scaling Illusion: MCP mixes stateful and stateless operations without clear distinction. There are no cache control mechanisms. No standardized operation semantics that enable safe retry. Tool invocations can’t be safely retried or load-balanced without understanding their side effects.
REST taught us in 2000 that statelessness enables horizontal scaling. MCP asks Sarah’s team to build session affinity systems from scratch, hitting backends with identical repeated queries because there’s no caching layer.
The Debugging Nightmare: Remember Sarah at 3 AM? With gRPC, distributed tracing would show her the exact failed call in minutes. She’d see the full request flow, latency at each step, the specific error that caused the problem. The trace ID would correlate logs across all services.
With MCP, she’s reconstructing what happened from fragments. No trace context propagation. No correlation IDs. No standard way to track a request across tool boundaries.
One scenario takes 30 minutes. The other takes 3 days.
gRPC solved this in 2016. MCP asks us to debug like it’s 1999.
The Library Trap
Point out any of these gaps to MCP advocates, and you’ll hear a familiar refrain:
“Oh, but there’s mcp-oauth-wrapper that adds authentication!”
“Check out mcp-tracing-extension for distributed tracing!”
“Company X open-sourced mcp-schema-generator that solves the IDL problem!”
This response pattern is the problem.
Here’s the problem: when you need a bunch of third-party libraries just to handle basic enterprise requirements, you haven’t actually created a protocol. You’ve just set the stage for fragmentation—the very thing protocols are meant to solve.
Think about the implications for enterprise architects: Out of five rival MCP authentication libraries, which one should become our standard? Are these libraries actively supported? Will they still be around in two years? Are they compatible with each other? If Tool A relies on mcp-auth-lib and Tool B uses mcp-security-wrapper, can they work together? When security flaws emerge, who takes ownership? How do we maintain uniform implementation standards across a 200-member engineering organization?
gRPC doesn’t need a third-party tracing library—it’s built in. REST doesn’t require external caching semantics—they’re part of HTTP. CORBA didn’t need community-maintained IDL generators—the ORB vendors provided them.
The enterprise cost of this fragmentation is staggering: Instead of training developers on one protocol, you’re training them on MCP plus a dozen semi-compatible extensions. Instead of conducting a single security audit, you’re auditing multiple authentication libraries. Instead of managing a single vendor relationship, you’re managing relationships with several open-source projects of varying quality and commitment.
This is the fragmentation that MCP was supposed to prevent, emerging with extra steps.
The $50,000 Question
It’s the end of the month, and Sarah’s CFO has a simple question: “Why did OpenAI bill us $50,000 last month?”
Can Sarah tell him which department’s MCP tools drove that cost? Which specific tool calls? Which individual users or use cases?
No. MCP provides no mechanism for this basic operational requirement. No token counting at the protocol level. No cost attribution headers. No quota management.
Cloud providers learned this lesson decades ago. Every AWS API call can be tagged, attributed, and cost-tracked. Every Google Cloud operation flows into detailed billing breakdowns.
MCP asks enterprises to consume expensive AI resources with 1990s-level cost visibility.
Sarah is flying blind, unable to optimize or even understand where the money goes.
The Patchwork Protocol Problem
The 2025-03-26 protocol revision tells a story. Read it like release notes, and you’ll see a list of everything enterprises discovered was missing in production:
- OAuth 2.1 support (because authentication wasn’t in the original release)
- Tool annotations for distinguishing read-only from destructive operations
- Session management improvements
- Progress notifications
These aren’t enhancements. They’re admissions of premature release.
Consider tool annotations. MCP now supports marking tools as “read-only” or “destructive,” but this feature was introduced after early adopters had already built systems without such distinctions. It’s capability-based security as an afterthought, retrofitted after production incidents rather than designed from first principles.
The security additions are particularly telling. Authentication wasn’t an oversight—it was deemed unnecessary for the initial release. This reveals a fundamental misunderstanding of enterprise requirements.
No Fortune 500 company would deploy a database without authentication. Yet MCP advocates expected them to connect AI to critical business tools without it.
Even now, OAuth 2.1 only applies to HTTP transports. The stdio transport relies on environment variables for credentials—a 1970s approach that lacks the granular access control modern enterprises require.
What Still Works (And What Doesn’t)
Here’s the paradox: MCP’s simplicity is both its greatest strength and its fatal flaw.
Where MCP Excels:
- Rapid prototyping and experimentation
- Internal tools with controlled environments
- Development and testing scenarios
- Small-scale deployments with manual oversight
Where MCP Breaks:
- Multi-region production deployments requiring service discovery
- High-throughput systems needing connection pooling
- Compliance environments requiring audit trails
- Cost-sensitive operations needing attribution
- Mission-critical systems where debugging time matters
The gap between these two lists is the distance between a demo and production.
The Choice We’re Making
Sarah eventually solved her production incident. It took three days. The root cause? A type coercion bug between JavaScript and Python implementations of MCP. The fix took ten minutes once she found it.
The cost? Approximately $200,000 in lost transactions, customer churn, and engineering time. Plus the intangible cost: trust. Customers who experienced fraud detection failures don’t just come back because you fixed a bug.
This is the bill coming due for ignoring 40 years of distributed systems lessons.
But here’s what’s interesting: this isn’t inevitable. MCP doesn’t have to become CORBA—heavy, complex, intimidating. The recent additions show that the maintainers are listening. They’re just playing catch-up with problems that earlier protocols solved decades ago.
What MCP Needs Now:
Immediate (to prevent the next Sarah)
- Type safety mechanisms that catch errors at build time, not 3 AM
- Distributed tracing with correlation IDs baked into the protocol
- Capability-based authorization beyond simple tool annotations
- Standardized audit trail formats for compliance
- Schema versioning independent of protocol version
Short-term (to enable production deployments)
- Service discovery for dynamic environments
- Connection pooling and persistent connections
- Binary protocol options for high-throughput scenarios
- Deadline propagation to prevent cascade failures
- Comprehensive error taxonomies with retry semantics
Long-term (to become truly enterprise-ready)
- Bidirectional streaming for complex interactions
- Built-in rate limiting and quota management
- SLA enforcement mechanisms
- Cost attribution for token usage
- Workflow orchestration primitives
The Real Question
This article isn’t actually about MCP. It’s about a pattern we keep repeating in technology.
Every few years, a new technology emerges that promises to make everything simple. The demos are compelling. The adoption is rapid. The hype is deafening.
And then it hits production.
The question isn’t whether MCP will add these features. The pattern of retrofitting critical capabilities suggests they will. The question is: how many Sarahs will debug production incidents at 3 AM before they do?
How many companies will discover that adding security, observability, and proper error handling after the fact is like adding airbags to a car after it’s crashed?
How many times will we choose to learn these lessons the hard way?
What This Means For You
If you’re evaluating MCP today:
Don’t ask if it works for demos. Ask if it works at 3 AM on a Tuesday when your production system is down and customers are churning.
Ask about observability, not just functionality. Ask about failure modes, not just happy paths. Ask who’s responsible when things break.
If you’re already using MCP:
Start building the observability layer you’ll need before the incident happens. Implement correlation IDs. Build cost attribution. Create fallback mechanisms.
Don’t wait for the protocol to catch up. Your production systems can’t afford to.
If you’re building on MCP:
Understand that you’re building on a foundation that’s still being poured. The simplicity that made adoption easy will become a liability as you scale.
Plan for the gaps. Budget for the workarounds. Know that you’re an early adopter, with all the risks that entails.
The Industry We’re Building
We have forty years of experience in distributed systems. We know which patterns enable reliable, secure, scalable operations. These aren’t academic exercises—they’re solutions to problems that cost real companies real money when systems fail.
The window is closing. As enterprises hit MCP’s limitations, they’ll build proprietary solutions. So will software vendors. The fragmentation that MCP aimed to prevent will emerge anyway, just with extra steps and wasted effort.
The AI industry has a choice: learn from four decades of RPC evolution or repeat every painful mistake.
Based on the current trajectory—with critical features being bolted on as afterthoughts—we’re choosing repetition.
And somewhere, right now, another Sarah is about to get a 3 AM call about a production incident that could have been prevented.
The only question is: will it be your Sarah?
The analysis in this article is based on the MCP specification as of 2025-06-18 and real-world deployment experiences from enterprises adopting AI infrastructure.