If you’ve ever been elbow-deep in spaghetti code, cursing the data gods for making even the simplest AI integrations feel like trying to plug a PS/2 mouse into a modern MacBook, then you might want to sit down (with a strong beverage): enter the Model Context Protocol, or MCP for short. It’s not another three-letter acronym doomed to haunt your documentation repo. According to AI sage and RitewAI founder Will Hawkins, MCP is “incredibly useful”—and not just because it sounds like the name of a CGI villain from a 1980s arcade game.
Picture this: your AI-powered system needs a bite of real-time traffic data, a sprinkle of Slack-channel drama, and maybe a dash of GitHub commit history—without causing your data warehouse to have a nervous breakdown. What you’d usually end up with is a tangled mess of APIs, webhooks, and brittle middleware, each update risking digital Jenga. Will Hawkins, on the AI Agent & Copilot Podcast, has a better analogy: MCP is the USB-C connector for the AI world. One protocol to unite them all, he explains, allowing data to flow effortlessly between your AI, whatever models you’re using, and the dizzying array of data sources modern businesses demand.
Let’s pause for a moment and let that sink in—because anyone in IT knows that “seamless” is usually just shorthand for “not quite as impossible as last year.” In theory, MCP replaces all the “bespoke integrations” you’ve been hand-cranking since your first post-SharePoint job. Now, with a single protocol, any AI agent can not only slurp down relevant data but also initiate actions—say, rerouting a delivery truck because MCP noticed an overturned chicken truck on the expressway.
Before you throw your integration toolkit into the recycling bin, Hawkins warns there’s still work to do. MCP isn’t a magic wand. You’ll have to wire up support on the back end and ensure your data sources actually speak MCP. But the beauty lies in the flexibility: you can roll it out locally (in your own dusty server closet) or remotely (in your cloud empire), depending on what makes your compliance officer twitch less.
Deployment, as Hawkins suggests, is best approached by experimenting with low-risk datasets first. Not only does that spare your reputation in case of catastrophic glitches, but it lets you learn the quirks of MCP before letting it anywhere near your precious customer records or payroll data.
And here’s where every security professional’s ears perk up: yes, there are known vulnerabilities. (Did you expect anything less from a protocol that dabbles in so many dark data corners?) But, Hawkins says, prudent developers can defend against most of them with the usual security best practices—strong auth, isolation, audit logging. Consider this a polite warning: if you trust “out of the box” defaults, you might as well hand your user data over to your nearest competitor.
Here’s the rub—while the protocol is almost seductively powerful, it requires a bit of institutional backbone and the maturity to not plug every damn thing into everything else just because you can. (This is your friendly reminder that not every Slack message needs to kick off a three-stage approval workflow, regardless of how much the workflow automation vendor insists it does.)
It’s a rare day when a developer calls something “incredibly useful” and isn’t being sarcastic. Microsoft’s move to tightly embrace MCP, and to make it a first-class citizen in such a wide array of their platforms, is a not-so-subtle push for MCP to become the universal grease for every AI-powered workflow. If your Microsoft rep is talking about “synergies,” this is probably what they mean.
But not everyone’s leaping in with both feet. Hawkins notes that Meta and Apple are playing it safe, watching from the sidelines as MCP finds its feet. This isn’t surprising—Apple, after all, refuses to put USB-C on the iPhone until the EU strong-arms them. When it comes to interoperability, Apple prefers its users in a gilded cage. Meta, on the other hand, would probably rather invent the Metaverse Protocol first.
It’s not just about making life easier for the existing players. Hawkins predicts a Cambrian explosion of new business models, from vendors hawking “data as a service” to ISVs and partners eager to bolt MCP onto their own products. There’s even talk of MCP turning into the lingua franca of data access for AI agents everywhere, and with the likes of Google’s Agent to Agent (A2A) protocol offering complementary support, MCP’s vision of universality starts looking less like PowerPoint vaporware and more like an actual roadmap.
Of course, with great universality comes great risk—standardizing on a protocol can just as easily lead to monoculture vulnerabilities as it can to interoperability. If every digital door in your enterprise is accessible via MCP, you’d better make damn sure your locks are up to snuff.
Yet, Hawkins injects a note of caution: despite its promise, there’s a competence gap. Building high-quality, security-conscious tools for AI agents takes a skill set that may not be equally distributed across the IT landscape. Organizations can’t just bolt MCP onto their ERP systems and call it a day—they need teams who actually understand both the protocol and the business context.
This is more than just a technical quibble. As AI becomes more embedded in real-world business processes, data quality becomes the choke point, not the protocol. Enterprises basking in the glow of their shiny new AI workflow will quickly discover that MCP is only as valuable as the data it unleashes. If your client’s inventory is a pile of orphaned spreadsheets, expect a tidal wave of half-baked decisions and “unexpected” results.
Yet, for organizations willing to invest in education, experimentation, and continuous security hygiene, MCP could prove to be the connector that ties together not just tooling, but entire business philosophies. Agile AI, adaptive workflows, smart automation—these become less buzzwordy when AI agents can actually talk to the stuff that matters, in real time.
So, whether you’re an architect with a stack of flowcharts, an IT consultant looking for the next big thing to put on your LinkedIn headline, or just an over-caffeinated sysadmin tired of building madcap integrations, MCP is worth a closer look. Just remember: with every shiny new technology, it pays to keep your cynicism close and your security settings closer. The age of the AI agent is here—and thanks to MCP, they might finally know which way the traffic is flowing.
Now, if only there were a protocol for mopping up spilled coffee after your AI agent books three meetings at once. Progress, right?
Source: Cloud Wars AI Agent & Copilot Podcast: AI Expert Will Hawkins Labels Model Context Protocol (MCP) 'Incredibly Useful'
MCP: The Swiss Army Knife of AI Integration
Picture this: your AI-powered system needs a bite of real-time traffic data, a sprinkle of Slack-channel drama, and maybe a dash of GitHub commit history—without causing your data warehouse to have a nervous breakdown. What you’d usually end up with is a tangled mess of APIs, webhooks, and brittle middleware, each update risking digital Jenga. Will Hawkins, on the AI Agent & Copilot Podcast, has a better analogy: MCP is the USB-C connector for the AI world. One protocol to unite them all, he explains, allowing data to flow effortlessly between your AI, whatever models you’re using, and the dizzying array of data sources modern businesses demand.Let’s pause for a moment and let that sink in—because anyone in IT knows that “seamless” is usually just shorthand for “not quite as impossible as last year.” In theory, MCP replaces all the “bespoke integrations” you’ve been hand-cranking since your first post-SharePoint job. Now, with a single protocol, any AI agent can not only slurp down relevant data but also initiate actions—say, rerouting a delivery truck because MCP noticed an overturned chicken truck on the expressway.
Before you throw your integration toolkit into the recycling bin, Hawkins warns there’s still work to do. MCP isn’t a magic wand. You’ll have to wire up support on the back end and ensure your data sources actually speak MCP. But the beauty lies in the flexibility: you can roll it out locally (in your own dusty server closet) or remotely (in your cloud empire), depending on what makes your compliance officer twitch less.
Untangling the Implementation Web
If your organization runs on a cocktail of GitHub, Google Drive, Slack, Postgres, and mystery legacy systems (bless your heart), you’re not alone. Hawkins points out that there are existing MCP connections for many such platforms, and the list is growing. The act of deploying MCP will vary depending on your infrastructure and security posture—think of it as more of a moving target than a set-and-forget switch.Deployment, as Hawkins suggests, is best approached by experimenting with low-risk datasets first. Not only does that spare your reputation in case of catastrophic glitches, but it lets you learn the quirks of MCP before letting it anywhere near your precious customer records or payroll data.
And here’s where every security professional’s ears perk up: yes, there are known vulnerabilities. (Did you expect anything less from a protocol that dabbles in so many dark data corners?) But, Hawkins says, prudent developers can defend against most of them with the usual security best practices—strong auth, isolation, audit logging. Consider this a polite warning: if you trust “out of the box” defaults, you might as well hand your user data over to your nearest competitor.
Customers Are Curious—And Nervous
Customer interest, it turns out, is already bubbling around MCP. Data platform companies see it as a natural evolution, an easier path for their customers to operationalize data, automate annoyances, and speed up those painful quarterly business reviews. But, as Hawkins notes, buyer excitement comes tempered with security anxiety. With the recent upgrades in authentication methods, businesses are encouraged to play with MCP using lower-stakes data sets, building confidence (and institutional memory) before connecting anything that sets off alarms in the boardroom.Here’s the rub—while the protocol is almost seductively powerful, it requires a bit of institutional backbone and the maturity to not plug every damn thing into everything else just because you can. (This is your friendly reminder that not every Slack message needs to kick off a three-stage approval workflow, regardless of how much the workflow automation vendor insists it does.)
The Microsoft Factor: “Copilots” With Jet Fuel
No discussion of the modern AI landscape would be complete without Microsoft’s fingerprints all over the blueprint, and MCP is no exception. Hawkins heaps praise on Microsoft for its rapid MCP support across Copilot Studio, Azure AI Engineer, and GitHub Copilot. The podcast’s show-and-tell moment is straight out of a support engineer’s fever dream—Hawkins, stuck on a coding problem, lets GitHub Copilot’s MCP adapter loose on a cryptic REST API error. The bot reaches out, searches the web, retrieves the needed documentation, and (wait for it) solves the coding issue in a matter of moments. Cue the sound of a thousand Stack Overflow tabs quietly closing in shame.It’s a rare day when a developer calls something “incredibly useful” and isn’t being sarcastic. Microsoft’s move to tightly embrace MCP, and to make it a first-class citizen in such a wide array of their platforms, is a not-so-subtle push for MCP to become the universal grease for every AI-powered workflow. If your Microsoft rep is talking about “synergies,” this is probably what they mean.
But not everyone’s leaping in with both feet. Hawkins notes that Meta and Apple are playing it safe, watching from the sidelines as MCP finds its feet. This isn’t surprising—Apple, after all, refuses to put USB-C on the iPhone until the EU strong-arms them. When it comes to interoperability, Apple prefers its users in a gilded cage. Meta, on the other hand, would probably rather invent the Metaverse Protocol first.
Vendor Support: Infinite Connectors, Infinite Possibilities (And Headaches)
Here’s where the picture gets really interesting. The blossoming of MCP isn’t just a win for nimble in-house developers—there’s a veritable ecosystem ripening around it. Zapier, everyone’s favorite “if-this-then-that” for the grownup SaaS world, now supports MCP, too. Suddenly, the humble protocol is poised to become not just an industry standard, but possibly an ISO standard, sprinkled atop your next compliance audit checklist.It’s not just about making life easier for the existing players. Hawkins predicts a Cambrian explosion of new business models, from vendors hawking “data as a service” to ISVs and partners eager to bolt MCP onto their own products. There’s even talk of MCP turning into the lingua franca of data access for AI agents everywhere, and with the likes of Google’s Agent to Agent (A2A) protocol offering complementary support, MCP’s vision of universality starts looking less like PowerPoint vaporware and more like an actual roadmap.
Of course, with great universality comes great risk—standardizing on a protocol can just as easily lead to monoculture vulnerabilities as it can to interoperability. If every digital door in your enterprise is accessible via MCP, you’d better make damn sure your locks are up to snuff.
Partner Opportunities: The New Consulting Gold Rush
For Microsoft partners and the broader community of IT professionals, MCP isn’t just another technical curiosity—it’s a potential gold rush. Hawkins is especially bullish on opportunities for partners to advise customers and build custom MCP integrations. After all, while MCP may be the universal connector, there’s still an insatiable demand for bespoke data choreography. Whether you’re a salty ISV whose idea of fun is building plugins, or a consultant who lives for billable hours orchestrating cloud migrations, MCP could be your next ticket to relevance—and to a nice new ergonomic chair.Yet, Hawkins injects a note of caution: despite its promise, there’s a competence gap. Building high-quality, security-conscious tools for AI agents takes a skill set that may not be equally distributed across the IT landscape. Organizations can’t just bolt MCP onto their ERP systems and call it a day—they need teams who actually understand both the protocol and the business context.
Data Quality: Still the Elephant in the Virtual Room
It’s fashionable in the AI world to pretend that all data is created equal—that the right protocol can smooth rough edges and sweep quality concerns under the rug. Hawkins shatters this fantasy, reminding us that “garbage in, garbage out” is an eternal law, not just a turn of phrase. MCP can’t fix your flaky data sources or the embarrassing gaps in your system of record. It just makes it easier (and faster) for your AI agents to reach all the parts that might be, well, less sanitary than management would like to believe.This is more than just a technical quibble. As AI becomes more embedded in real-world business processes, data quality becomes the choke point, not the protocol. Enterprises basking in the glow of their shiny new AI workflow will quickly discover that MCP is only as valuable as the data it unleashes. If your client’s inventory is a pile of orphaned spreadsheets, expect a tidal wave of half-baked decisions and “unexpected” results.
Risk, Reward, and the Harsh Light of Reality
If you’re reading this and wondering where the catch is—congratulations, you’ve been in IT long enough to develop healthy skepticism. MCP, for all its promise, is not a panacea. Security remains a moving target; every door it opens is one more that needs a deadbolt. The protocol’s very flexibility—its ability to plug any data source into any AI agent—brings with it a new kind of chaos, the sort that only the truly brave or truly naive will approach without a battle plan.Yet, for organizations willing to invest in education, experimentation, and continuous security hygiene, MCP could prove to be the connector that ties together not just tooling, but entire business philosophies. Agile AI, adaptive workflows, smart automation—these become less buzzwordy when AI agents can actually talk to the stuff that matters, in real time.
Conclusion: MCP’s Future, and Why You Should Care
Despite my best efforts, I can’t find much to excoriate in MCP’s ambitions (though I reserve the right to grumble at the first major security SNAFU). It’s rare to find a protocol that serves both developers and business leaders so directly—minimizing integration toil while maximizing the value trapped inside siloed data. Where Microsoft leads, others will inevitably follow, and if even Zapier is jumping aboard, it might be time for the rest of us to stop memeing about the “one connector to rule them all” and actually get to work.So, whether you’re an architect with a stack of flowcharts, an IT consultant looking for the next big thing to put on your LinkedIn headline, or just an over-caffeinated sysadmin tired of building madcap integrations, MCP is worth a closer look. Just remember: with every shiny new technology, it pays to keep your cynicism close and your security settings closer. The age of the AI agent is here—and thanks to MCP, they might finally know which way the traffic is flowing.
Now, if only there were a protocol for mopping up spilled coffee after your AI agent books three meetings at once. Progress, right?
Source: Cloud Wars AI Agent & Copilot Podcast: AI Expert Will Hawkins Labels Model Context Protocol (MCP) 'Incredibly Useful'
