5 key questions your developers should be asking about MCP

The Model Context Protocol (MCP) has turn into one of the talked-about developments in AI integration since its introduction by Anthropic in late 2024. For those who’re tuned into the AI house in any respect, you’ve doubtless been inundated with developer “scorching takes” on the subject. Some suppose it’s one of the best factor ever; others are fast to level out its shortcomings. In actuality, there’s some reality to each.

One sample I’ve observed with MCP adoption is that skepticism usually offers option to recognition: This protocol solves real architectural issues that different approaches don’t. I’ve gathered an inventory of questions beneath that mirror the conversations I’ve had with fellow builders who’re contemplating bringing MCP to manufacturing environments. 

1. Why ought to I take advantage of MCP over different options?

In fact, most builders contemplating MCP are already aware of implementations like OpenAI’s custom GPTs, vanilla operate calling, Responses API with operate calling, and hardcoded connections to companies like Google Drive. The query isn’t actually whether or not MCP absolutely replaces these approaches — underneath the hood, you might completely use the Responses API with operate calling that also connects to MCP. What issues right here is the ensuing stack.

Regardless of all of the hype about MCP, right here’s the straight reality: It’s not an enormous technical leap. MCP primarily “wraps” current APIs in a manner that’s comprehensible to massive language fashions (LLMs). Certain, plenty of companies have already got an OpenAPI spec that fashions can use. For small or private tasks, the objection that MCP “isn’t that massive a deal” is fairly honest.

The sensible profit turns into apparent while you’re constructing one thing like an evaluation instrument that wants to connect with information sources throughout a number of ecosystems. With out MCP, you’re required to jot down customized integrations for every information supply and every LLM you wish to assist. With MCP, you implement the information supply connections as soon as, and any appropriate AI consumer can use them.

2. Native vs. distant MCP deployment: What are the precise trade-offs in manufacturing?

That is the place you actually begin to see the hole between reference servers and actuality. Native MCP deployment utilizing the stdio programming language is lifeless easy to get operating: Spawn subprocesses for every MCP server and allow them to discuss by stdin/stdout. Nice for a technical viewers, troublesome for on a regular basis customers.

Distant deployment clearly addresses the scaling however opens up a can of worms round transport complexity. The unique HTTP+SSE method was changed by a March 2025 streamable HTTP replace, which tries to cut back complexity by placing all the things by a single /messages endpoint. Even so, this isn’t actually wanted for many firms which can be more likely to construct MCP servers.

However right here’s the factor: A couple of months later, assist is spotty at finest. Some purchasers nonetheless anticipate the previous HTTP+SSE setup, whereas others work with the brand new method — so, in the event you’re deploying right this moment, you’re in all probability going to assist each. Protocol detection and twin transport assist are a should.

Authorization is one other variable you’ll want to think about with distant deployments. The OAuth 2.1 integration requires mapping tokens between exterior id suppliers and MCP periods. Whereas this provides complexity, it’s manageable with correct planning.

3. How can I be certain my MCP server is safe?

That is in all probability the most important hole between the MCP hype and what you truly have to sort out for manufacturing. Most showcases or examples you’ll see use native connections with no authentication in any respect, or they handwave the safety by saying “it makes use of OAuth.” 

The MCP authorization spec does leverage OAuth 2.1, which is a confirmed open customary. However there’s all the time going to be some variability in implementation. For manufacturing deployments, concentrate on the basics: 

• Correct scope-based entry management that matches your precise instrument boundaries 
• Direct (native) token validation
• Audit logs and monitoring for instrument use

Nonetheless, the most important safety consideration with MCP is round instrument execution itself. Many instruments want (or suppose they want) broad permissions to be helpful, which suggests sweeping scope design (like a blanket “learn” or “write”) is inevitable. Even with out a heavy-handed method, your MCP server could entry delicate information or carry out privileged operations — so, when doubtful, follow one of the best practices really useful within the latest MCP auth draft spec.

4. Is MCP price investing sources and time into, and can or not it’s round for the long run?

This will get to the guts of any adoption choice: Why ought to I trouble with a flavor-of-the-quarter protocol when all the things AI is transferring so quick? What assure do you could have that MCP will likely be a strong selection (and even round) in a yr, and even six months? 

Properly, have a look at MCP’s adoption by main gamers: Google helps it with its Agent2Agent protocol, Microsoft has built-in MCP with Copilot Studio and is even including built-in MCP features for Home windows 11, and Cloudflare is very happy that will help you fireplace up your first MCP server on their platform. Equally, the ecosystem progress is encouraging, with lots of of community-built MCP servers and official integrations from well-known platforms. 

In brief, the educational curve isn’t horrible, and the implementation burden is manageable for many groups or solo devs. It does what it says on the tin. So, why would I be cautious about shopping for into the hype?

MCP is essentially designed for current-gen AI programs, that means it assumes you could have a human supervising a single-agent interplay. Multi-agent and autonomous tasking are two areas MCP doesn’t actually tackle; in equity, it doesn’t actually need to. However in the event you’re on the lookout for an evergreen but nonetheless by some means bleeding-edge method, MCP isn’t it. It’s standardizing one thing that desperately wants consistency, not pioneering in uncharted territory.

5. Are we about to witness the “AI protocol wars?”

Indicators are pointing towards some stress down the road for AI protocols. Whereas MCP has carved out a tidy viewers by being early, there’s loads of proof it received’t be alone for for much longer.

Take Google’s Agent2Agent (A2A) protocol launch with 50-plus trade companions. It’s complementary to MCP, however the timing — simply weeks after OpenAI publicly adopted MCP — doesn’t really feel coincidental. Was Google cooking up an MCP competitor once they noticed the most important title in LLMs embrace it? Perhaps a pivot was the appropriate transfer. But it surely’s hardly hypothesis to suppose that, with options like multi-LLM sampling quickly to be launched for MCP, A2A and MCP could turn into opponents.

Then there’s the sentiment from right this moment’s skeptics about MCP being a “wrapper” reasonably than a real leap ahead for API-to-LLM communication. That is one other variable that can solely turn into extra obvious as consumer-facing functions transfer from single-agent/single-user interactions and into the realm of multi-tool, multi-user, multi-agent tasking. What MCP and A2A don’t tackle will turn into a battleground for one more breed of protocol altogether.

For groups bringing AI-powered tasks to manufacturing right this moment, the sensible play might be hedging protocols. Implement what works now whereas designing for flexibility. If AI makes a generational leap and leaves MCP behind, your work received’t endure for it. The funding in standardized instrument integration completely will repay instantly, however preserve your structure adaptable for no matter comes subsequent.

Finally, the dev neighborhood will resolve whether or not MCP stays related. It’s MCP tasks in manufacturing, not specification class or market buzz, that can decide if MCP (or one thing else) stays on prime for the subsequent AI hype cycle. And admittedly, that’s in all probability the way it needs to be.

Meir Wahnon is a co-founder at Descope.