Model Context Protocol (MCP) servers are reshaping how AI systems interact with the world. Think of them as standardized “ports” that let AI tools securely and intelligently plug into other systems, whether it’s file storage, databases, productivity tools, or developer environments. As the AI ecosystem matures, these servers are becoming critical infrastructure, quietly powering some of the most advanced capabilities in tools like Claude, Copilot, and Replit’s Ghostwriter.
This post digs into the Model Context Protocol (MCP), how servers implement it, why it’s becoming the USB‑C of AI, and what it means for developers building the next generation of intelligent apps.
Building AI agents that can actually do things like file operations, database queries, sending emails; used to mean stitching together dozens of APIs with brittle prompt engineering or custom plugins. That’s changing fast.
In late 2024, Anthropic introduced the Model Context Protocol, or MCP, a standardized way for AI models to interface with external tools and data through live, structured communication. By mid-2025, support for MCP had spread rapidly across leading AI labs, enterprise agent platforms, and developer tools.
At its core, MCP servers make AI systems more useful, more trusted, and more integrated. They allow a model to not only understand context but to act in it through a controlled, permission-aware pipeline.
What Are MCP Servers?
An MCP server is a backend service that exposes structured functionality to AI models via the Model Context Protocol. You can think of it as an API server, but with a few twists:
- Persistent Session Context: Instead of one off calls, MCP servers maintain a session-aware context that allows ongoing, stateful interaction with a model.
- Model-Driven Usage: They’re designed not for humans to call directly, but for models to interact with dynamically based on changing conversation goals.
- Explicit Permissions and Contracts: MCP servers advertise their capabilities using a schema, and every call requires model specified intent and user approval.
The result is something like an “app store” for models. But instead of downloading apps, models can dynamically discover and interact with tools, subject to user consent and structured interfaces.
Examples of available MCP servers include:
- File system access (e.g., documents, screenshots, local search)
- Code analysis and repo operations (e.g., git diff, test runs)
- Cloud apps (e.g., Slack, Notion, Google Sheets)
- Data querying tools (e.g., SQL databases, CRM systems)
Each of these lives as a “server” that the model can call into as needed during a session.
How MCP Works: Under the Hood
The protocol uses a few core components:
1. Registry
A shared registry exposes which MCP servers are available in a given session. This lets models know what tools they can use, what methods are available, and under what constraints.
2. Client
Usually part of an AI tool (e.g. Claude Desktop, VS Code, ChatGPT with plugins), the client handles:
- Routing requests from the model to the appropriate server
- Managing user permissions and security policies
- Translating model intents into structured JSON-RPC requests
3. Server
This is the actual tool or integration. It provides:
- A service definition (
.well-known/mcp/service.json) - Real-time execution of function calls (e.g. listFiles, createBranch)
- Context callbacks, such as when new files or metadata are available
4. Data Source
Behind each server may be a live data source (filesystem, database, web app, or process) that the server mediates access to.
The client-server interaction is usually powered by JSON-RPC 2.0, with OpenRPC-style definitions to describe functions, types, and usage constraints.
Real-World Examples of MCP Servers in Action
Claude Desktop on Windows
Anthropic’s Claude Desktop integrates tightly with the local operating system using MCP. When you ask Claude to summarize a PDF or pull up a file, it’s not searching a static index. Instead, it’s calling into an MCP server that:
- Lists your recent files
- Reads the contents of documents
- Converts them into a context the model can use
Because this access is structured and permissioned, the user sees exactly which file Claude is using and what it’s doing with it.
2. GitHub Copilot with Agent Mode
In developer environments like VS Code, GitHub Copilot’s “Agent” mode now uses MCP to call out to Git, run test suites, or inspect build logs. An MCP server here might expose commands like:
getCurrentBranch()listModifiedFiles()runTestSuite(name: string)
So instead of treating Git commands as opaque strings, the model can reason about them semantically and operate with full visibility and context.
3. Replit Ghostwriter
Replit’s Ghostwriter uses MCP-style architecture to bridge code generation with live project state. The model can fetch environment variables, manage file trees, and interact with the terminal, all via an MCP server interface. This enables a far more fluid dev experience, where suggestions are grounded in what’s really going on.
4. Enterprise AI Agents
Companies like Block, Sourcegraph, and OpenAI’s early enterprise clients use MCP servers to connect models to internal databases, knowledge bases, and ticketing systems. For example:
- A customer support agent can look up previous interactions from a CRM system
- A sales assistant can generate reports from live pipeline data
- A compliance tool can review logs or flag anomalies in real-time
Instead of pushing all internal data into model context, the model fetches what it needs, when it needs it, through MCP.
Victor! Simplify This For Me
Before USB-C, every device had a different port. You needed adapters, special drivers, and often had to restart your system. Remember the “good” old days of taking a week to install a printer?
Before MCP, connecting AI to tools was just like that:
- Custom APIs
- Ad hoc plugins
- Brittle prompt hacks
MCP changes this. Just like USB-C lets your laptop charge your phone, connect a display, and transfer files (all from one port) MCP allows models to access many tools through one protocol. It’s one standard interface for many capabilities. In short, a Wrapper Design Pattern on top of the different tools.
Even better, MCP is designed with the model as the primary user, not a human. That means every detail (schema discovery, permissions, context refresh) is meant to help the model act responsibly and transparently.
Use Cases Across Industries
Coding
- Auto-refactor tools grounded in project structure
- Context-aware debugging assistants
- Live feedback during test writing and CI/CD
Productivity
- Meeting summarizers that pull calendar and email data via MCP
- Document assistants that retrieve relevant files and version history
- Task planners that interact with Notion, Trello, or Slack
Business Operations
- Agents that query live sales pipelines or financial metrics
- Legal tools that search and compare contracts in structured ways
- HR tools that analyze engagement surveys or help onboard employees
Creative Work
- AI art directors that pull from your local asset libraries
- Music assistants that browse samples and templates on your machine
- Writing tools that pull research, outline structure, and feedback in real time
How MCP Changes AI App Development
Before MCP, building an AI-powered app meant one of two things:
- Stuff all relevant data into the model’s context window (which quickly hits limits, especially on mono repos)
- Hack together custom API calls triggered by model output (which can be fragile or insecure)
MCP flips this around by making context and action first-class citizens. Instead of dumping data into the model or forcing it to guess how to interact with tools, you give it a structured interface and permissions to call what it needs, when it needs it.
Key Advantages:
- Modularity: Developers can write new MCP servers as pluggable modules.
- Discoverability: Models can explore the capabilities of an MCP server on the fly.
- Security: Access is scoped and user-approved, not hardcoded.
- Scalability: As more servers become available, AI clients can dynamically combine them.
This is why folks are calling MCP “the new plugin architecture” for AI. But unlike the old plugin models (which were closed, manual, and limited to one provider), MCP is open, dynamic, and model-agnostic.
How to Build or Use an MCP Server
If you’re a developer, here’s a simplified roadmap:
1. Choose the Tool You Want to Expose
For example:
- A file browser
- A Notion workspace
- Your own internal API
2. Implement the MCP Server Interface
You’ll need to:
- Expose a discovery endpoint:
/.well-known/mcp/service.json - Define capabilities with JSON-RPC 2.0 and OpenRPC schemas
- Implement logic to handle the methods you expose
There are SDKs available in Python, TypeScript, and Go. The Composio dev guide is a good place to start.
3. Register Your Server with an MCP Client
In tools like Claude Desktop or ChatGPT plugins, you declare your MCP server and its endpoint. It becomes visible to the model during sessions.
4. Handle Authentication and Permissions
MCP clients will typically ask the user to approve each capability or data source. You can also implement:
- Rate limiting
- Scope constraints
- Token-based auth
- Logging for traceability
5. Test Model Interactions
Once connected, test how the model discovers and uses your server. Refine the interface to make function names and metadata clear and intuitive.
The Security and Permission Model
This is where MCP stands out.
Instead of running custom scripts or raw code, models call structured functions. These calls:
- Are described in advance
- Can be previewed and approved by users
- Are constrained to safe inputs and outputs
Tools like MCP Guardian and SecMCP (from Anthropic and academic teams) let you define guardrails like:
- What data a model can read/write
- Whether certain methods need multi-factor confirmation
- Redaction policies before data is sent back to the model
This security model is what makes MCP viable for enterprise-grade applications, not just personal use.
The Future of MCP: Where This Is Going
MCP is still new, but its trajectory feels similar to what happened when HTTP became the standard for web interactions or when USB unified device ports.
What’s Next?
1. Cross-Model Compatibility
The goal is for models from OpenAI, Anthropic, Google, and others to all speak the same “language” when calling external tools. Just like every web browser understands HTTP, every AI client will understand MCP. That means you could write a tool once and have it work with ChatGPT, Claude, and Gemini.
2. More Servers, More Capabilities
Just like the early App Store era, we’ll see a rush of MCP servers across:
- Enterprise SaaS (Salesforce, HubSpot, SAP)
- DevOps (Docker, Kubernetes, CI/CD)
- Creative tools (Figma, Adobe, Blender)
- Consumer apps (Spotify, WhatsApp, calendar tools)
Eventually, there could be a public index of servers much like npm or PyPI for AI capabilities.
3. Autonomous Agents
Right now, most AI agents need to be told what to do. But MCP lays the groundwork for agents that can autonomously:
- Check which tools they have access to
- Chain together actions across tools
- Report back progress transparently
This could dramatically reduce the friction in workflows like research, coding, report generation, or even orchestrating marketing campaigns.
Challenges and Open Questions
Despite its potential, MCP is not a magic bullet. There are still some hurdles to clear:
1. Security and Trust
If a model can read your emails or change a config file, you better be sure it’s doing exactly what you approved. Permissioning UI/UX and access logs will be critical.
2. Context Limits
Even with MCP, models still have to ask the right questions. If a model doesn’t know a file exists or forgets to call a method, it can miss important information. Ongoing research is needed in tool selection reasoning.
3. Standardization and Fragmentation
Right now, most MCP support comes from Claude and Anthropic tools. While OpenAI and DeepMind are reportedly adopting similar standards, full cross-model alignment will take time.
4. Server Quality
Just like APIs can be badly designed, MCP servers can vary in how usable and helpful they are. Community best practices and tooling will be needed to make great servers easy to write and share.
Some Final Thoughts
MCP servers may not be flashy, but they’re foundational. They offer a clean, secure, and extensible way for AI to interact with the real world; beyond just generating text. With adoption growing fast, it’s worth paying attention whether you’re building AI tools or just trying to understand where this space is headed.
In a sense, MCP completes the picture of what we’ve wanted from AI: not just something that says smart things, but something that can do smart things, responsibly and seamlessly.