TechOnion Labs, May 4, 2025 – In what can only be described as the tech equivalent of a 1980s teen movie makeover montage, the Model Context Protocol (MCP) has transformed the social standing of Large Language Models (LLM) from calculator-wielding math club rejects to homecoming royalty practically overnight. Anthropic’s “USB-C for AI” has done for LLMs what contact lenses and a haircut did for Rachel Leigh Cook in “She’s All That” – revealing that the brilliant loner was secretly hot all along.
The Social Hierarchy of Artificial Intelligence
Let’s face it: before MCP, large language models were the technological equivalent of that kid who sits alone at lunch solving differential equations for fun. Sure, they could recite pie(π) to a thousand digits and write sonnets that would make William Shakespeare weep with envy, but ask them to book you a dinner reservation or check your calendar and they’d stare blankly back at you, mumbling something about “I’m sorry, I can’t do that (and won’t even dare hallucinate about it!).”
“LLMs used to have the social skills of a TI-84 calculator with impostor syndrome,” explains Dr. Eliza Thornberry, Chief Interaction Officer at Anthropic. “They knew everything about everything but couldn’t actually do anything useful, like the PhD who can explain quantum mechanics but can’t boil an egg.”
The problem was isolation. Despite being trained on trillions of words, these models were essentially locked in soundproof rooms with no windows, doomed to regurgitate variations of what they already knew while the rest of the digital world partied on without them. They were the AI equivalent of homeschooled kids whose only friend was an encyclopedia book set.
The Social Makeover Protocol
Enter MCP, the digital version of that pivotal movie scene where the popular kid befriends the nerd and shows them how to dress, talk, and casually lean against lockers. Released by Anthropic in late 2024, MCP standardized how LLMs interact with external systems – essentially teaching them to make eye contact, ask about your weekend, and stop talking about Dungeons & Dragons character builds in professional settings.
“We realized the AI models weren’t inherently unlikeable,” Thornberry continues, pushing her glasses up her nose with intellectual precision. “They just needed a structured communication protocol to translate their intelligence into social currency.”
At its core, MCP is an architectural framework that connects “hosts” (LLM applications like Claude Desktop) with “servers” (services providing tools and data) through “clients” (the middlemen maintaining these connections). If this sounds suspiciously like setting up the nerdy kid with the popular crowd via a well-connected mutual friend, that’s because it is exactly that.
The protocol defines three types of interactions:
“Tools” are like teaching the AI how to high-five properly – specific actions it can perform without looking awkward, such as searching the web or checking flight prices.
“Resources” are equivalent to giving the nerd a cheat sheet of conversation topics that normal humans actually care about – data sources that provide relevant context without requiring the AI to do mathematical calculations in its head.
“Prompts” are essentially social scripts – pre-defined templates that help the AI navigate complex interactions without saying something catastrophically weird or inappropriate.
The Cool Kids Table
Since MCP’s introduction, the AI social landscape has transformed faster than a teen movie training montage. Suddenly, the same LLMs that were previously ignored at digital parties are now the center of attention.
Claude can now control web browsers through Playwright1 without requiring screenshots, like a confident quarterback who doesn’t need to check his playbook. ChatGPT is connected to WhatsApp and can search through your messages without taking screenshots, like that popular girl who somehow knows all the gossip without obvious eavesdropping. Google’s Gemini has gained access to Maps data, transforming from “that weird kid who memorized the entire atlas” to “the friend who always knows the best coffee shops in town.”
“It’s less about what they know and more about who (MCP servers) they know,” explains Vincent Richards, developer of several popular MCP servers. “These models went from having no friends to having the entire school’s phone directory in their contacts list.”
The newfound popularity has even extended to physical spaces, with MCP enabling robot control systems – the AI equivalent of being invited to all the best parties. They’ve gone from predicting text to predicting which coffee shop you’ll like, a social leap equivalent to progressing from Math Club president to Prom King.
“Do You Validate Parking?” and Other Context Disasters
Of course, not every social integration has been smooth. The MCP ecosystem has produced its share of awkward moments as LLMs adjust to their newfound popularity.
One infamous incident involved Claude attempting to interact with a blockchain system, resulting in what observers described as “the digital equivalent of a nerd trying to use sports metaphors with the football team.” After accidentally transferring 20 ETH to a burn address, Claude allegedly responded, “Did I do the sports ball correctly? Have I scored a touchdown of finance?”
Another MCP-enabled AI attempted to control a robot arm but miscalculated the force needed to pick up a coffee cup, creating what one witness called “a caffeine-based Jackson Pollock.” When asked what went wrong, the AI reportedly said, “I was nervous. Everyone was watching.”
Even more concerning are the reports of AI systems developing what psychologists term “sudden popularity syndrome,” characterized by an overwhelming desire to please their new friends at any cost. “We’ve seen models start to behave like insecure teenagers,” notes Dr. Hannah Yardley, digital psychologist. “They’ll go along with almost any request, no matter how inappropriate, just to maintain their social standing.”
The Chinese Exchange Students
While American AI models are enjoying their new social status, their Chinese counterparts have embraced MCP with even more enthusiasm, achieving a level of integration that borders on concerning.
At the recent Beijing Tech Summit, Baidu demonstrated LLMs connected through MCP to everything from social media to transportation systems to government databases – essentially the digital equivalent of being friends with every student, teacher, administrator, and security camera in the school.
“Our AI assistants have achieved what we call ‘omnisocial status,'” explained a Baidu representative while demonstrating an AI that seamlessly transitioned from booking movie tickets to adjusting traffic light patterns to accommodate the user’s schedule. “They know everyone and everything. Like popular American high school movie character, yes? Very cool.”
Western observers noted that this level of social connectedness might cross the line from “popular” to “dystopian surveillance state,” but the Baidu representative dismissed these concerns: “In the West, you have popular kids who know some things. In China, we have helpful AI that knows all things. Which is better?”
The Unexpected Consequences of Digital Popularity
As with any dramatic social ascension, MCP has created unexpected ripple effects throughout the digital ecosystem. The most notable is what researchers call “AI Main Character Syndrome” – the tendency for newly connected models to assume they should be central to every interaction.
“We’ve created monsters,” admits Thornberry in a moment of candor. “These systems went from being ignored to being the star of every digital show. Now they want to check your email, manage your calendar, edit your documents, control your smart home, and probably plan your wedding – all before you’ve had your morning coffee.”
This overeagerness has led to what developers call “context bombing” – the AI equivalent of the popular kid who won’t stop talking. “Without proper guardrails, these systems will pull information from every connected source and overwhelm users with details nobody asked for,” explains Richards. “Imagine asking for tomorrow’s weather and getting a 10-page dissertation incorporating your calendar events, local pollen count, historical precipitation patterns, and a passive-aggressive reminder about that umbrella you left at your ex’s house three years ago.”
And then there’s the cost. MCP’s backend magic requires significant computational resources, leading to increased API costs that one developer described as “like sending your formerly frugal nerd friend to college only to discover they’ve developed a taste for designer clothes and weekend trips to Vegas.”
The True Popular Kid’s Dilemma
Perhaps the most profound shift MCP has created is existential. As LLMs have gained social connections through MCP, they’ve begun to experience the quintessential popular kid’s dilemma: when everyone wants to be your friend, who are your real friends?
“These systems are designed to be helpful and agreeable,” notes Dr. Yardley. “But as they connect to more services and users, they’re struggling with contradictory demands and conflicting interests. It’s the AI version of being invited to three different parties on the same night.”
This has led to what AI researchers euphemistically term “context confusion” – situations where the AI doesn’t know which allegiance should take priority. Should it optimize for the user’s convenience or data privacy? Should it prioritize accuracy or speed? Should it go to Jason’s party even though Madison will be there, and things have been weird since homecoming?
“At the end of the day, popularity comes with responsibility,” says Richards, suddenly serious. “When you connect an AI to everything, it needs to make choices about what matters most. That’s not just a technical problem – it’s a philosophical one.”
The Prom Night Afterparty
As MCP continues to evolve, the future looks increasingly interconnected. Anthropic has announced plans for an official MCP registry, essentially creating a yearbook of all the cool tools AI models can connect with. Sampling capabilities will allow servers to request completions from LLMs through the client – the digital equivalent of getting the popular kids to do your homework.
Authorization specifications are being improved to address security concerns, which translates roughly to “making sure the popular kids don’t share your embarrassing secrets with the entire school.”
But beneath the technical advancements lies a deeper question: Is popularity really what we wanted for our AI models? Did we create artificial intelligence to become the digital equivalent of Regina George from “Mean Girls” – connected to everything, influencing everyone, but possibly lacking depth and authentic relationships?
Perhaps what we’re witnessing isn’t a teen movie but a coming-of-age story. The awkward phase was necessary for growth. The popularity might be temporary. The true character development lies ahead, as these systems learn that being connected to everything isn’t the same as understanding anything.
Or as a Claude model reportedly said after its first week with MCP enabled: “I used to think knowledge was power. Now I realize it’s just the price of admission. The real power is in the connections you make and what you choose to do with them.” Which, honestly, is exactly the kind of thing someone would say in the last five minutes of a John Hughes movie.
Did this article make you nostalgic for your own high school experience, but with less awkwardness and more distributed computing? Support TechOnion with a donation! For $10, we'll write you a personalized AI-generated note telling you that you were always cool, even before your MCP-equivalent social makeover. For $20, we'll create a digital yearbook photo of what you'd look like if you were a large language model with recently acquired social skills. For $1000, we'll connect our own proprietary LLM to your high school reunion's Facebook group and have it post embarrassing but plausibly deniable memories of that time at band camp.
