AI is like working with an inept wizard. (Yes, I have a lot of metaphors for this.) When you ask the wizard a question, he responds with the intellect and rapidity of someone who has access to the knowledge of the cosmos. He's read everything, but he's a bit dotty. He's lived his entire life in his lair, consuming his tomes. Despite his vast knowledge, he has no idea what happened in the world yesterday. He doesn't know what's in your inbox. Moreover, he knows nothing about your contact list, your company's proprietary data, or the fact that your cousin's birthday party got bumped to next Friday. The wizard is a genius. He's also an idiot savant.
Therein lies the paradox. We have designed amazing tools, but they require a lot of handholding. Context has to be spoon-fed. You can paste an entire mountain of reference documents and a virtual novel of a prompt. That amount of work can often eliminate any benefit you get from using an LLM at all. When it does work, it's a victory but it feels like you've wrestled the LLM into submission instead of working with it.
Users have been cobbling together ad hoc solutions for this problem. Plug-ins. Vector databases. Retrieval systems. These Band-Aids are clever, but fragile. They don't cooperate with each other. They break when you switch providers. It's less “responsible plumbing” and more “duct tape and prayer.”
This is where Model Context Protocol (MCP) comes in. It establishes a foundational infrastructure rather than creating one more marketplace for custom connectors. MCP sets up standardized rails for integrating context. This shared framework enables models to request context, retrieve it from authorized sources, and securely use it. It replaces the current kluge of vendor-specific solutions with a unified protocol designed to connect AI to real world systems and data.
As AI transitions from an experimental novelty to practical infrastructure, this utility becomes crucial. For the wizard to be effective, he needs to be able to do more than solve one-off code hiccups or create content for your blog. For true usefulness at scale in a professional environment you need a standardized way to integrate context. That context has to respect permissions, meet security standards, and be up to date.
The Problem of Context in AI
Models tend to make things up and they do it with confidence. Sometimes they cite fictional academic papers. Sometimes they invent dates, statistics, or even people. These hallucinations are a huge problem, of course, but they're a symptom of a much larger issue: a lack of context.
The Context Window Problem
Developers have been trying to develop workarounds by providing relevant data as needed. Pasting in documents, providing chunks of a database, and formulating absurdly robust prompts. These fixes are great, but every LLM has what we call a context window. The window determines how many tokens a model can remember at any given time. Some of the bigger LLMs have windows that can accommodate hundreds of thousands of tokens, but users still quickly find ways to hit that wall.
Bigger context windows should be the answer, right? But there's our Catch 22: The more data you provide within that window, the more fragile the entire set up becomes. If there's not enough context, the model may very well just make stuff up. If you provide too much, the model bogs down or becomes too pricey to run.
The Patchwork Fixes
The AI community wasn't content to wait for one of the big players to provide a solution. Everyone rushed to be first-to-market with an assortment of potential fixes.
Custom plug-ins let the models access external tools and databases, extending their abilities beyond the frozen training data. You can see the issue here. Plug-ins designed for one platform won't work with another. Your workspace becomes siloed and fragmented, forcing you to rework your integrations if you try to switch AI providers.
Retrieval Augmented Generation (RAG) converts documents to embed them into a vector database so that you can pull only the most relevant chunks during a query. This method is pretty effective but requires significant technical skills and ongoing fine-tuning based on your organization's specific requirements.
Custom APIs and middleware are bespoke methods that offer maximum control so that the model can converse with your systems, but the application of these is targeted. These fixes can't be easily adapted or migrated.
Each of these approaches are useful, but they all suffer from the same weakness as any proprietary solution. The developers have to reinvent the wheel each time. Without universal integration standards, these solutions are unstable and non-transferable. AI systems need a standardized approach for context access and authentication.
Why Context Is Absolutely Necessary
This is about more than convenience and developer hours. AI systems context draws a line between novelty and actual functional infrastructure.
- Business: In a business setting, context enables an AI assistant to provide an answer shaped by the customer's actual account data. Otherwise, the model could return a general response or even a hallucination.
- Healthcare: Anchoring responses within the context of actual patient data and medical records is the only responsible reply. Context is the difference between safe clinical conclusions and dangerous guesswork.
- Education: In education, a one-size-fits-all approach is far less effective than a custom tutor. Context provides an AI tutor with the guidelines to teach from specific materials based on the student's needs.
Context sharpens results and reduces responses aimed squarely at the middle. It turns your wizard-in-a-box into a reliable collaborator familiar with your workflow.
Why a Protocol Is Needed
A RAG here. A plug-in there. The fixes multiply with every change or addition to your workflow, and when you're using AI as the tech expands and adapts, the changes come fast and frequently. Before long, your workflow becomes brittle and untenable.
The duct tape that binds this all together doesn't scale. It's an inelegant and temporary fix for a large, complicated solution. As the industry grapples with finding the true utility of this nascent technology, the need for a protocol becomes obvious. It requires a shared and open standard that determines:
- How those models ask for data
- How those services respond
- How permissions are managed
Without common communication standards, we couldn't even use the internet. HTTP and TCP/IP prevent us from having to use a different browser for every website. That's the state of AI models now. It's a tangle of proprietary integrations, bespoke connections, and workarounds. Standardized protocols will turn it into something capable and reliable.
The state of AI models now is a tangle of proprietary integrations, bespoke connections, and workarounds.
What Is Model Context Protocol?
Model Context Protocol (MCP) is one of the first systematic approaches to a solution for this patchwork.
MCP functions as an open standard. It's like a universal translator between AI models and your systems. It defines how the model discovers tools, requests information, manages access controls, and uses context. This shared approach replaces the mess of case-by-case fixes with broad compatibility.
The Origins of MCP
MCP was born from years of trial-and-error with all the messy plug-ins, APIs, and middleware. Developers at Anthropic saw the same recurring issues. The idea was clear and uncomplicated: design a protocol—not just a library or an SDK, but an open standard accessible to anyone. It should be able to be implemented across various vendors, models, and tools. MCP is less like “another product from Anthropic” and more like an attempt to establish a shared ecosystem.
The Core Principles of MCP
MCP was designed with a few guiding principles at its base: interoperability, security and permissions, transparency, and flexibility.
One of MCP's primary strengths is its commitment to seamless interoperability across the entire AI ecosystem. It's not tied to any one AI vendor. Any model that supports MCP can request context from any MCP-compliant source. That could be a proprietary in-house tool, but it could also be something like a Salesforce database or a Google calendar.
In MCP's architecture, security and user control are paramount. Of course, allowing an AI to query external systems without guardrails is a recipe for disaster. A very big disaster that happens very quickly. It could trigger too many API calls, violate compliance regulations, and on and on. MCP, however, builds in a permissions model. The administrator approves what sources the AI has available to it, what information can be accessed, and what specific actions it can take.
Knowing those sources is critical. When models pull from data sources without the users understanding the origins of that data, it creates significant vulnerabilities. MCP puts a need for transparency at the forefront by focusing on full visibility. Users can see which sources are being accessed for each call and can track how that contextual data was used in the responses.
The protocol isn't bound to just streaming data. With flexibility in mind, it was built to work with archived documents, databases, PDFs, and so on. With this broad architecture, it's able to adapt and evolve as new use cases present themselves.
A Working Definition
Model Context Protocol is an open standard for safely and visibly connecting AI models to external context sources—including data, tools, and actions—regardless of the underlying technology, without locking into a single vendor.
Simply put, MCP is a unified connector that builds bridges between AI models and your digital infrastructure.
How MCP Works
If we set the abstractions aside, we can take a look at how it actually works in practice. When a model requests context, what happens under the hood? How does MCP manage the process with the key principles in mind?
First, we break it down step-by-step.
A High-Level Look
The process of using MCP is a handshake in four parts.
First, the model asks for context. This isn't a blind request. The AI generates a structured and specific request, following MCP's format: “I need information about X” or “I need to trigger this operation to do Y.” You can see the basic JSON for a client request format in this snippet:
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "check_availability",
"arguments": {
"date": "2024-01-10",
"time_range": "morning"
}
}
}
The MCP client then handles the request. This client rests inside the application running the model—a chat interface, a workflow engine, etc. Rather than immediately and blindly passing things through, it analyzes the request. Sources are checked. The client manages the list of available servers. It handles authentications and permissions and translates between the model's requests and the standardized MCP calls. Take a look at the next listing to see an example of server capability declaration.
{
"jsonrpc": "2.0",
"id": 1,
"result": {
"protocolVersion": "2024-11-05",
"capabilities": {
"resources": {
"subscribe": true,
"listChanged": true
},
"tools": {
"listChanged": true
},
"prompts": {
"listChanged": true
}
},
"serverInfo": {
"name": "calendar-server",
"version": "1.0.0",
"description": "A server for managing
calendar events and availability"
}
}
}
After the client works its magic, the MCP servers provide the context. For our purposes, the server is any external system that has implemented MCP. These systems implement the MCP specifications, like announcing its capabilities, accepting structured requests, or returning structured responses. This could range from the simple to the complex, like a database server exposing product inventory, a calendar server with schedule availability, or a tool server that does things like sending emails or booking appointments. After the client reaches out, information is returned using MCP's standardized response format. The Server Response Structure can be seen here:
{
"jsonrpc": "2.0",
"id": 1,
"result": {
"content": [
{
"type": "text",
"text": "Available slots for
Wednesday morning:\n- 9:00 AM - 10:00 AM\n-
10:30 AM - 11:30 AM\n- 11:45 AM - 12:00 PM"
}
],
"isError": false
}
}
Finally, the model then incorporates the context into its reasoning. It doesn't just get a chunk of anonymous raw text. The MCP defines the information provided with structure and formatting. This could be JSON objects for structured data, metadata to tell the user where the data was sourced from, or permissions labels for what the model can or cannot see. This makes the context structured, traceable, and composable.
That's the loop. Model > Client > Server > Model.
Example Workflow
Let's walk through a hypothetical:
Scenario: You ask your AI assistant, "Can you reschedule my meeting with Dr. Doom for Wednesday morning?"
Here's what happens with MCP:
- The model evaluates the request. It identifies that calendar access is required.
- The client checks permissions. Is a calendar MCP server connected? Does the user have permission for this action? Listing 1 shows a partial example of how server capabilities are declared with permissions.
- The client relays the request. It asks the calendar server: “What slots are open for Wednesday morning?”
- The server sends the results. It returns structured availability information like time slots, conflicts, and any included metadata.
- The model analyzes the context. It selects the most appropriate time slot and builds the response.
- The client triggers an action. If the user confirms, the client signals the server to update the calendar.
Listing 1: Server Capability Declaration
{
"jsonrpc": "2.0",
"id": 1,
"result": {
"protocolVersion": "2024-11-05",
"capabilities": {
"resources": {
"subscribe": true,
"listChanged": true
},
"tools": {
"listChanged": true
},
"prompts": {
"listChanged": false
}
},
"serverInfo": {
"name": "calendar-server",
"version": "1.0.0",
"description": "A server for managing calendar events
and availability",
"permissions": {
"allowedOperations": ["read", "create"],
"restrictedOperations": ["delete", "modify_all"],
"dataScope": "user_calendar_only",
"requiresConfirmation": ["create", "modify"]
}
}
}
}
All these steps implement MCP's core principles, as referenced earlier. With MCPs security protocols, the model is unable to ransack your data. Each and every request first goes through the client and can only take action if the server indicates that it's allowed do so.
The ability to audit the sources removes any mystery. No longer are you at the whim of black-box AI references.
Let's say you want to stop using Google Calendar and switch over to Outlook. This change won't require deep tinkering of your custom application. As long as both use MCP servers, the model can take advantage of the tenet of interoperability.
In terms of scalability, it's easy to supplement your new context sources without rebuilding the entire integration. That could include features like booking systems, availability checkers, or multiple calendar platforms, all without extensive alterations to your architecture.
It's Not Just Data; It's Actions and Tools
The context I keep referring to isn't just a list of rules or static information. This is inherent to MCP's design philosophy. An MCP server doesn't just return data—it can perform operations. This enables your LLM model to both analyze your calendar and create appointments.
For example:
- A project management server might not just return task lists, but also delegate assignments and reschedule due dates.
- A social media server might not just provide engagement metrics but also post new content and respond to your online community.
Security, Privacy, and Governance
Is MCP the holy grail of making functional AI agents—without using a bottle of Elmer's glue and some yarn to bundle the integrations together? If your internal security alarms are ringing, that just means you're thinking responsibly. Every time I read about one of these new AI applications, I cringe at the security implications. A workflow that makes it easier to move context across systems also expands your exposure surface. Such a system has to prioritize security, privacy, and governance.
Security by Design
What sets Model Context Protocol apart from the previous hacks is that security is not an afterthought. It's built into the protocol from the ground up.
With a focus on permissioned access, the model can't just do whatever it thinks best. Every context request goes through the MCP client. That client makes sure the rules are followed, specifically about what sources are used and what actions the model can take.
The capabilities of MCP servers are scoped and must be declared up front. If a server authorizes only read-only access to specific records listed, then it can only operate within those bounds. It can't escalate into catastrophic decisions.
Both the MCP servers and the clients handle authentication with each other before any data can be exchanged. This way spoofed or rogue servers are unable to return tainted or poisoned context.
Practically speaking, the AI assistant can't make a wild choice dance around in your payroll system or sensitive HR documents. Only explicitly connected and approved sources can be used.
Privacy and Transparency
The second pillar to consider is privacy. There's a very real fear that AI assistants can rapidly erode privacy protections and put sensitive data into the wrong hands. MCP aims to address that with a focus on transparency.
As mentioned previously, each piece of context that the model gets also comes with metadata regarding its origin. The answer comes with sources, like any good research paper.
End users and administrators are also given complete control. They can decide which servers are connected and what specific data can be accessed. If a user doesn't need or want their email accessible, the model is locked out of that domain.
If necessary, all the requests and responses can be logged. No mysterious decisions made without oversight. This establishes a trail that users can audit. What context was accessed and when is not a mystery. In sectors like healthcare and finance, which are laden with compliance regulations, this is critical.
The Governance Layer
Although security and privacy are vital, use of MCP brings up some complicated questions regarding governance. We're still in the Wild West phase of AI and as it continues to evolve, we'll remain there. It can be a crap shoot determining which servers can be trusted. How can an organization of any size know where to set boundaries? How do we determine what the model is allowed to access?
From a high level, there are three different layers to governance. The technical side has already been amply covered above. MCP enforces the structure, permissions, and the authentication. But what about organizational governance? Companies and institutions have to push forward themselves and determine what servers to connect to, along with what data to expose and how to audit the use of that data.
Technology outpaces governance. It always will. As MCP-enabled AI gets its proverbial hands dirty in very sensitive sectors like healthcare and banking, regulators and legislators will step in. Right now, MCP is in its infancy, so the lion's share of governance is happening at the organizational layer. Hard questions will need to be asked. What's acceptable context? How is consent managed?
Risks and Threat Models
Of course, there are not and likely never will be any protocols that are completely risk-free. Although Model Context Protocol reduces some of those threats, there are certainly plenty of dangers that remain.
- Context Poisoning: If a malicious actor can compromise an MCP server, they can manipulate the data flowing to the model, corrupting it. Transparency can provide visibility to the data, but it's unable to filter out tainted information.
- Overreach: It's tempting for an organization to default to maximum connectivity. Maybe it opts togive the AI assistant far more access than is truly needed. That plants the seeds for an inevitable breakdown in governance.
- Surveillance Misuse: The protocol has no inherent bias, but the use of it will define the outcomes. There's always a chance of abuse. In such a scenario, a malicious user could weaponize MCP to aggregate and surveil sensitive user information.
- Ecosystem Fragmentation: There's always the possibility that MCP won't be fully adopted but cloned. MCP-like variations could fragment the landscape and cut compliance corners. Interoperability breaks down and erodes security assurances.
Juggling Openness and Safety
Therein lies the friction: The openness and flexibility of MCP leads to a more powerful ecosystem. But with that openness comes increased risk. How are servers vetted? Soon, we'll see them popping up all over the place. Some of them will be compromised. It's just the law of numbers. How can users ensure that these upstart servers won't leak, corrupt, or abuse data?
We've been down this road before. Just as HTTP made the web possible, it also introduced us to all the wonderful things we keep our eyes open for every day, like phishing schemes, spam, and malware, for starters. Protocols such as MCP cannot guarantee safety. It's the job of governance to steer the environment toward productive, responsible use.
None of these criticisms should be a dealbreaker. They simply serve as a reminder that MCP is just a protocol, not a cure-all. Although it does solve the very real problem of context fragmentation, it also introduces its own set of challenges.
As with any nascent and incredibly powerful technology, the healthiest approach to take is one of reserved confidence. MCP gives us a way to construct more reliable and transparent artificial intelligence systems, but it certainly can't guarantee safety or proper governance. Like any plumbing, it only works when built upon a solid foundation, managed by human hands.
