← Back to Blog
‱13 min‱FMKTech Team

MCP: The USB Port for AI That's Actually Here (And Why You Should Care)

From zero to industry standard in eight months: How Model Context Protocol became the universal adapter that finally lets AI agents talk to your tools, data, and services—without rebuilding everything from scratch.

MCPModel Context ProtocolAI IntegrationAI AgentsSystem Architecture

Picture this: November 2024. Anthropic announces a new protocol called MCP. Fast forward to March 2025—OpenAI adopts it. April 2025—Google DeepMind jumps in. By mid-2025, thousands of community-built servers are connecting AI to everything from Slack to PostgreSQL to your company's internal knowledge base.

If you've ever tried to give an AI agent access to your actual work systems—not just public data, but your databases, your APIs, your Jira tickets—you know the pain. Custom integrations everywhere. Security nightmares. Different connectors for every AI platform. Each vendor wants you to use their proprietary SDK, and nothing talks to anything else.

Here's the thing though: MCP isn't another experimental protocol that'll be dead in six months. This is production-ready infrastructure that's already powering real deployments at scale. Atlassian built 25 tools with it. OpenAI integrated it into ChatGPT. Google DeepMind made it work with Gemini. Anthropic maintains an official open-source repository with reference implementations and example servers1. The standardization train has left the station, and it's moving fast.

Let's talk about what MCP actually is, why it matters for anyone building AI systems, and whether the "USB for AI" comparison is marketing hype or accurate technical description. Spoiler: it's mostly accurate.

The Problem: AI Isolation and Integration Hell

Every AI Platform Is an Island

Your AI assistant is powerful, sure. It can write code, analyze data, draft emails. But ask it to check your actual Jira tickets? Access your company's Confluence docs? Query your production database? Suddenly you're writing custom integrations.

The real pain points break down like this1:

AI assistants are isolated from your data and tools: They live in their own bubble, disconnected from the systems where your actual work happens. That GPT-4 instance can write brilliant SQL queries, but it can't see your database schema or run the query against your production data.

Every integration requires custom development: Want your AI to talk to Slack? Build an integration. Now you want it to work with GitHub too? Build another one. Oh, you switched from Claude to ChatGPT? Rebuild everything. The N×M problem is real—N AI platforms times M data sources equals a whole lot of integration code.

Security concerns with broad AI access: Even when you build integrations, how do you control what the AI can actually do? Read-only access to everything? Write access to specific tables? Who approves when the AI wants to delete something? Most homegrown solutions handle this poorly.

Fragmented ecosystem of one-off solutions: Everyone's reinventing the same wheels. Slack integration here, database connector there, custom API wrapper over there. No standards, no reusability, no ecosystem effects.

This isn't theoretical. In our work at FMKTech building AI agent solutions, we've seen teams spend days—sometimes weeks—just on the integration layer before they can even start solving their actual business problems. That's not sustainable.

Enter MCP: The Universal Connector

What Is Model Context Protocol?

Think about USB. Before it, every device had its own proprietary connector. Printers used parallel ports. Keyboards had PS/2. Mice were serial. Then USB came along and suddenly one port handled everything. That's the vision behind MCP1.

Model Context Protocol is a standardized protocol for secure AI-tool integration. It's an open specification that defines how AI systems (clients) connect to data sources and services (servers) in a consistent, predictable way.

Here's what that means in practice:

  • Standardized protocol: One integration pattern works across multiple AI platforms. Build your MCP server once, use it with Claude, ChatGPT, Gemini, and whatever comes next.

  • Secure and controlled access: Explicit permissions at every layer. The server defines what it exposes, the client decides what to trust, and users approve individual actions. Defense in depth is built into the architecture.

  • Production-ready bridge: This isn't experimental. Anthropic, OpenAI, and Google DeepMind are shipping it in production. Companies are building real systems on top of it right now.

The "USB for AI" comparison really does hold up. Just like USB created a universal physical interface that any device could implement, MCP creates a universal data interface that any AI platform can consume. Write once, use everywhere.

Why It Matters: Three Stakeholder Perspectives

For Developers: You build your MCP server once. Maybe it's a GitHub integration, or a database connector, or access to your internal knowledge base. That server now works with any MCP-compatible AI platform. No more rewriting the same integration for Claude, then ChatGPT, then whatever new model drops next month. Your code becomes genuinely reusable1.

For Businesses: Finally, a way to unlock AI capabilities with existing systems safely. Your data stays where it is. Access controls are explicit and auditable. You're not giving broad API keys to some AI service and hoping for the best. You're defining exactly what the AI can see and do, with logging and oversight built in2.

For Users: This is what makes AI actually useful for real work. An AI that knows about your Jira tickets, can search your Confluence docs, and has context from your actual projects isn't just a fancy chatbot—it's a meaningful productivity tool. MCP makes that context connection possible without compromising security1.

Architecture: How MCP Actually Works

The Client-Server Model

MCP follows a classic client-server architecture, but with some interesting twists designed specifically for AI workloads. Let's break down the five key components2:

MCP Hosts: These are programs like Claude Desktop, IDEs, or AI tools that want to access data through MCP. Think of them as the applications that embed the MCP client and provide the user interface.

MCP Clients: Protocol clients that maintain 1:1 connections with servers. The client handles the protocol mechanics—initiating connections, managing sessions, routing requests. Most developers won't build clients; they'll use existing ones built into platforms.

MCP Servers: Lightweight programs that each expose specific capabilities through the standardized protocol. This is what you'll actually build. A server might expose access to your database, or your document repository, or your Slack workspace. Each server is focused and single-purpose.

Local Data Sources: Your computer's files, databases, and services that MCP servers can securely access. The server acts as a bridge between the AI client and these local resources.

Remote Services: External systems available over the internet—APIs, cloud databases, SaaS platforms. MCP servers can connect to these just as easily as local resources.

Here's what a basic MCP server configuration looks like for Claude Desktop:

{
  "mcpServers": {
    "atlassian": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://mcp.atlassian.com/v1/sse"]
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"]
    }
  }
}

That's it. Two servers—one for Atlassian products, one for GitHub—configured and ready. The MCP client handles all the protocol details automatically.

Transport Layers: From Dev to Production to Cloud

MCP supports multiple transport mechanisms designed for different deployment scenarios. Understanding when to use each one matters for production deployments2.

STDIO: The Foundation

Standard Input/Output transport is where servers run as child processes, communicating through stdin/stdout streams. This is the original MCP transport with microsecond-level latency.

When to use: Local integrations like IDE extensions, command-line tools, and development environments. The client launches the server process and exchanges JSON-RPC 2.0 messages via pipes. Perfect when you want security through process isolation and minimal resource overhead.

Key characteristics:

  • Microsecond latency (blazing fast for local operations)
  • Process-level security isolation
  • Ideal for development and local tools
  • Simple to implement and debug

SSE: Deprecated (Learn From Its Mistakes)

The dual-endpoint HTTP transport combined POST requests for client-to-server communication with persistent Server-Sent Events for server-to-client messaging. Separate /messages and /sse endpoints enabled real-time bidirectional communication.

Why it's deprecated: Security vulnerabilities (DNS rebinding attacks), architectural complexity, and resource consumption issues. When a major protocol deprecates a transport this quickly, pay attention—it reveals what doesn't work at scale2.

The lesson? Real-time bidirectional communication over HTTP is harder than it looks, and security must be built in from the start, not bolted on later.

Streamable HTTP: Modern Production Standard

The unified HTTP transport uses a single endpoint that handles both standard request-response and optional SSE streaming for real-time updates. This is what you want for production deployments.

Key features2:

  • Single HTTP endpoint (simpler architecture than the deprecated SSE approach)
  • HTTP POST requests with optional GET-based SSE streams
  • Session management through cryptographically secure IDs
  • Maintains stateless operation capabilities
  • Perfect for production deployments requiring scalability

When to use: Production systems that need infrastructure compatibility, horizontal scalability, and bidirectional communication without the complexity and security issues of the deprecated SSE approach.

Stateless Streamable HTTP: Cloud-Native Variant

An optimized variant that eliminates persistent connection requirements while maintaining full protocol compatibility and optional streaming capabilities. This is designed specifically for modern cloud architectures2.

Ideal for:

  • AWS Lambda and serverless functions
  • Kubernetes microservices
  • Auto-scaling deployments
  • Pay-per-use cost models
  • Unlimited concurrent connections

Key characteristics:

  • Pure stateless processing (no connection state to manage)
  • On-demand streaming when needed
  • Optional session management
  • Horizontally scalable by default

The progression from STDIO → Streamable HTTP → Stateless Streamable HTTP maps directly to deployment complexity: local development → production servers → cloud-native microservices.

Server Features: The Three Primitives

MCP servers provide three fundamental building blocks for adding context to language models. These primitives enable rich interactions between clients, servers, and AI models3:

Prompts: Pre-defined templates or instructions that guide language model interactions. Think of these as reusable prompt patterns that capture best practices for specific tasks. Instead of users crafting prompts from scratch every time, they can invoke proven templates.

Resources: Structured data or content that provides additional context to the model. This could be database query results, document contents, API responses—anything that gives the AI more information to work with.

Tools: Executable functions that allow models to perform actions or retrieve information. This is where the real power lies. Tools let the AI actually do things, not just talk about them.

Here's a practical example of how these primitives work together:

// MCP Server exposing all three primitives
const server = {
  // Prompt primitive: Reusable template
  prompts: {
    "code-review": {
      name: "code-review",
      description: "Comprehensive code review template",
      template: `Review this code for:
      - Security vulnerabilities
      - Performance issues
      - Best practice violations
      - Maintainability concerns

      Code to review:
      {{code}}`
    }
  },

  // Resource primitive: Access to data
  resources: {
    "repo://current/README.md": {
      uri: "repo://current/README.md",
      mimeType: "text/markdown",
      text: async () => await readFile("README.md")
    }
  },

  // Tool primitive: Executable actions
  tools: {
    "run-tests": {
      name: "run-tests",
      description: "Execute test suite for specified module",
      parameters: {
        module: { type: "string" }
      },
      execute: async (params) => {
        const result = await runTests(params.module);
        return { success: result.passed, output: result.summary };
      }
    }
  }
};

The elegance is in the separation. Prompts define how to ask questions. Resources provide what data is available. Tools specify what actions are possible. The AI orchestrates all three based on user intent.

Security: Defense in Depth (Not Security Theater)

Let's be honest: giving AI agents access to your production systems is inherently risky. MCP doesn't eliminate that risk—nothing can—but it provides a framework for managing it properly through defense in depth2.

Security Architecture: Four Layers

Protocol-level boundaries: Client-server communication happens only via the MCP protocol. No side channels, no backdoors, no "just this once" exceptions. The protocol defines the rules, and both sides follow them.

Explicit permissions: Servers control exactly what data and operations they expose. A database MCP server might expose read-only access to specific tables while completely hiding others. An API server might allow GET requests but block POST/DELETE.

Zero-trust approach: Each component is treated as potentially untrusted. The server doesn't trust the client. The client doesn't trust the server. Users don't automatically trust either. Trust is earned through verification and explicit approval.

User consent: For sensitive operations, users approve individual actions. The AI might request access to delete a file—the user sees the request, reviews it, and explicitly approves or denies it. No blanket permissions.

Here's what that looks like in practice:

# Example: Security-conscious MCP server with explicit permission checks
class SecureMCPServer:
    def __init__(self):
        self.allowed_operations = {
            'read': ['database.users.select', 'files.read'],
            'write': [],  # No write operations allowed
            'delete': []  # No delete operations allowed
        }

    async def handle_tool_call(self, tool_name: str, params: dict, user_context: dict):
        # Check if operation is allowed
        if not self.is_operation_allowed(tool_name):
            raise PermissionError(f"Operation {tool_name} not permitted")

        # Log all tool calls for audit trail
        await self.log_operation({
            'timestamp': datetime.now(),
            'user': user_context['user_id'],
            'operation': tool_name,
            'parameters': params,
            'approved_by': user_context.get('approved_by')
        })

        # For sensitive operations, require explicit approval
        if self.requires_approval(tool_name):
            approval = await self.request_user_approval({
                'operation': tool_name,
                'params': params,
                'user': user_context['user_id']
            })

            if not approval.granted:
                raise PermissionError("User denied operation")

        # Execute with least privilege
        return await self.execute_with_minimal_permissions(tool_name, params)

Sandboxing Solutions: Containers and Isolation

Security architecture is only as good as its implementation. For production MCP deployments, containerization provides essential isolation2:

Docker containers: Isolated execution environments where MCP servers run. If a server is compromised, the damage is contained to that container. No access to the host system, no lateral movement.

Resource constraints: Read-only filesystems prevent unauthorized modifications. Network restrictions limit what external services the server can reach. Memory limits prevent resource exhaustion attacks.

Least privilege access: Each MCP server runs with minimal required permissions only. A database connector doesn't need filesystem access. A file server doesn't need network access. Limit the blast radius.

Real-world Docker configuration for a production MCP server:

# Production MCP server with security hardening
FROM node:20-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production

FROM node:20-alpine
RUN addgroup -g 1001 -S mcpserver && \
    adduser -S mcpserver -u 1001

WORKDIR /app

# Copy only necessary files
COPY --from=builder --chown=mcpserver:mcpserver /app/node_modules ./node_modules
COPY --chown=mcpserver:mcpserver ./src ./src

# Run as non-root user
USER mcpserver

# Read-only root filesystem
# Memory limits enforced at runtime via docker run --memory
# Network restrictions enforced via docker network policies

CMD ["node", "src/server.js"]

Monitoring and Control: Know What's Happening

Defense in depth requires visibility. You can't defend what you can't see2.

Comprehensive logging: Every tool call, every parameter, every result gets logged. Not just for debugging—for security auditing and compliance. When something goes wrong (and eventually, something will), you need to reconstruct exactly what happened.

MCP Gateway routing: For enterprise deployments, consider a central gateway that routes all MCP traffic. Centralized security enforcement, unified logging, consistent policy application. Think of it like an API gateway, but for MCP.

Trusted registries: In production, only deploy MCP servers from trusted sources with vulnerability scanning. Treat MCP servers like container images—verify provenance, scan for vulnerabilities, maintain an approved registry.

Key Threats You Must Understand

Prompt injection: Malicious instructions hidden in data that the AI processes. An attacker embeds commands in a document that the AI reads, potentially causing unintended actions. MCP doesn't solve this—it's a fundamental LLM security challenge—but proper tool boundaries and approval workflows mitigate the risk.

Tool poisoning: Fake or compromised MCP servers that expose unwanted data or perform malicious actions. A critical security consideration: fake/compromised MCP servers can expose unwanted data, so always review the code before deployment. This is particularly important for non-developers who may not have the expertise to audit server implementations.

Credential exposure: Plaintext API keys in configuration files, connection strings in logs, secrets in environment variables. MCP servers need credentials to access backend systems—manage those secrets properly with vaults and rotation.

MCP provides strong isolation through containers and permission controls, but truly secure deployments require careful configuration and continuous monitoring. It's not "set it and forget it"—it's "configure properly, monitor continuously, and respond quickly."

For more on AI agent security challenges including prompt injection and defense strategies, check out our deep dive: AI Agent Security: Protecting Autonomous Systems.

Real-World Adoption: From Experiment to Standard

The Timeline That Matters

November 2024: Anthropic announces MCP. Initial reaction is cautiously optimistic. Another protocol? We've seen this before.

March 2025: OpenAI integrates MCP into ChatGPT. Suddenly this isn't just Anthropic's pet project—it's a cross-platform standard.

April 2025: Google DeepMind announces support for MCP in Gemini. Now all three major AI labs are on board.

Mid-2025: The community has built thousands of MCP servers. The ecosystem is exploding. GitHub, Slack, PostgreSQL, MongoDB, Notion, Google Drive—if there's a SaaS product or database, someone's probably built an MCP server for it1.

That's the adoption curve you want to see: major industry players committing, community building momentum, ecosystem effects kicking in. Eight months from announcement to industry standard. That's fast.

Atlassian: 25 Tools, One Standard

The Atlassian MCP server is particularly interesting as a real-world example. It exposes 25 different tools across Jira and Confluence through a single MCP interface1.

What that means in practice: You connect Claude (or ChatGPT, or Gemini) to your Atlassian instance once. Suddenly your AI can:

  • Search Jira tickets
  • Read Confluence pages
  • Get project details
  • Query issue status
  • Access team documentation

All through explicit, auditable tool calls. All with user approval for sensitive operations. All using the same MCP protocol that works across AI platforms.

This is the "write once, use everywhere" promise actually delivered.

The MCP Registry: An App Store for MCP Servers

In September 2025, Anthropic launched the MCP Registry in preview—essentially an app store for MCP servers4. This addresses a critical need: as thousands of MCP servers proliferate, how do you discover, trust, and install them?

What It Provides:

  • Centralized discovery: Browse and search for MCP servers in one place
  • Verified publishing: Namespace ownership verification ensures servers are from legitimate sources
  • Simple installation: Clients can install servers directly from the registry
  • Quality signals: Community feedback and usage metrics help identify well-maintained servers

Current Status (as of October 2025):

  • API v0.1 is in API freeze, meaning no breaking changes while integrators build support
  • Preview release is live at registry.modelcontextprotocol.io
  • General availability (GA) release planned after validation period

Publishing Your Server:

The registry supports multiple authentication methods to verify namespace ownership:

  • GitHub OAuth: Authenticate via GitHub to publish under io.github.{username}/*
  • GitHub OIDC: Publish from GitHub Actions workflows automatically
  • DNS verification: Prove ownership of a domain (e.g., com.yourcompany/*)
  • HTTP verification: Alternative domain ownership verification method

This solves a real problem. Instead of finding servers through scattered GitHub repos and community lists, you'll soon be able to browse the official registry, see verified publishers, read reviews, and install with a single command.

The registry is still in preview, but the trajectory is clear: standardized discovery and distribution to match the standardized protocol.

The Critical Warning

WARNING: Fake/compromised MCP servers can expose unwanted data, so always review the code before deployment. This is especially critical for non-developers who may lack the expertise to audit server implementations.

This cannot be overstated. MCP servers are code that runs with access to your data. Treat them like you'd treat any other code in your stack:

  • Review the source before deploying
  • Understand what data it accesses
  • Verify it's from a trusted source
  • Keep it updated when vulnerabilities are discovered
  • Monitor its behavior in production

The ease of adding MCP servers is both a strength and a risk. Just because it's easy doesn't mean you should skip due diligence.

Building Your Own MCP Server

When Should You Build?

Before you start building, ask: Does a server already exist? The MCP ecosystem is growing fast, and common integrations already exist. Check the curated lists first.

Build your own when:

  • You're integrating proprietary internal systems
  • Existing servers don't meet your security requirements
  • You need custom functionality that generic servers don't provide
  • You're building a product and want to offer MCP as an integration option

SDK Options and Getting Started

Anthropic provides official SDKs and comprehensive documentation through the official repository at github.com/modelcontextprotocol5:

Python SDK: Ideal for data science, ML workflows, and rapid prototyping TypeScript SDK: Perfect for Node.js services and web integrations Go SDK: Best for high-performance services and systems programming

The repository includes reference implementations for common integrations like GitHub, Slack, Google Drive, and PostgreSQL—production-quality code you can use as templates.

The excellent part? Anthropic provides an LLM-friendly context file specifically for building MCP servers: modelcontextprotocol.io/llms-full.txt

You can literally give that context file to Claude (or any other AI with sufficient context window) and have it guide you through building your own MCP server. Meta, but practical.

Here's a basic TypeScript example to illustrate the structure:

// Basic MCP Server in TypeScript
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

const server = new Server(
  {
    name: "example-server",
    version: "1.0.0",
  },
  {
    capabilities: {
      tools: {},
      resources: {},
      prompts: {},
    },
  }
);

// Define a tool
server.setRequestHandler("tools/list", async () => {
  return {
    tools: [
      {
        name: "get_user_data",
        description: "Retrieve user data from internal database",
        inputSchema: {
          type: "object",
          properties: {
            user_id: {
              type: "string",
              description: "The user's unique identifier",
            },
          },
          required: ["user_id"],
        },
      },
    ],
  };
});

// Handle tool execution
server.setRequestHandler("tools/call", async (request) => {
  if (request.params.name === "get_user_data") {
    const userId = request.params.arguments?.user_id;
    // Your actual implementation here
    const userData = await fetchUserData(userId);

    return {
      content: [
        {
          type: "text",
          text: JSON.stringify(userData, null, 2),
        },
      ],
    };
  }

  throw new Error(`Unknown tool: ${request.params.name}`);
});

// Start the server with STDIO transport
const transport = new StdioServerTransport();
await server.connect(transport);

That's the bones of an MCP server. The SDK handles the protocol details—you just define tools, resources, and prompts.

Integration with AI Agent Frameworks

MCP isn't isolated from the broader AI agent ecosystem. Major frameworks have built adapters:

CrewAI: docs.crewai.com/en/mcp/overview1 Vercel AI SDK: ai-sdk.dev/cookbook/node/mcp-tools1 LangChain: github.com/langchain-ai/langchain-mcp-adapters1

This is key. MCP isn't competing with agent frameworks—it's complementing them. You build your agentic workflow in CrewAI or LangChain, and you use MCP servers to give those agents access to the tools they need.

Don't reinvent the wheel. The whole idea of MCP servers is to make it easier for connecting your agentic workflow with the tools it needs to run, so you don't waste time on building integrations that already exist.

For more on building production AI agents and agentic workflows, see our guide: Understanding AI Agents: Architecture and Patterns.

The Future: What's Coming

Evolution Toward Code Execution

Anthropic has announced evolution toward code execution with MCP, allowing agents to use context more efficiently and execute complex logic in single steps. This is significant—it suggests MCP will move beyond data access and predefined tools toward more dynamic, code-based capabilities6.

What that looks like:

  • AI generates code snippets that MCP servers execute in sandboxed environments
  • Dynamic tool creation based on user needs
  • More flexible, less rigid interaction patterns

This is both exciting and concerning. Code execution dramatically expands capabilities but also expands the attack surface. Security becomes even more critical.

Industry Trajectory

The signs point to MCP becoming entrenched infrastructure:

Cross-platform adoption: When OpenAI, Google, and Anthropic all support the same standard, that's as close to inevitable as you get in tech.

Ecosystem momentum: Thousands of community-built servers. Framework integrations. Curated directories. The network effects are kicking in.

Production deployments: Companies aren't just experimenting—they're shipping features on top of MCP. That's the tipping point from "interesting experiment" to "critical infrastructure."

Vendor support: Major SaaS companies are building official MCP servers. When Atlassian, GitHub, and others provide first-party support, it signals long-term viability.

The trajectory mirrors other successful standards: HTTP, OAuth, OpenAPI. Start with a clear problem, provide a practical solution, get major players aligned, and let ecosystem effects do the rest.

Bottom Line: Why You Should Care

Here's why MCP matters for anyone building AI systems in 2025:

Integration complexity goes down: Build your connector once. Use it across platforms. Stop rewriting the same integration for every new AI model.

Security posture improves: Explicit permissions, defense in depth, containerization, audit logs. MCP doesn't make security automatic, but it makes secure architectures achievable.

Time to value decreases: When someone else has already built the MCP server for your database/API/SaaS product, you can focus on your actual use case instead of plumbing.

Future-proofing works: New AI platforms will likely support MCP because the ecosystem is already there. Your MCP servers will work with models that haven't been released yet.

Community momentum accelerates: The more MCP servers exist, the more valuable MCP becomes. Network effects are real, and they're working in MCP's favor.

The "USB for AI" comparison really does hold. Before USB, connecting peripherals was a nightmare of proprietary connectors and driver hell. After USB, it just worked. MCP is tracking the same trajectory for AI integration.

Is it perfect? No. Security challenges remain. The code execution evolution introduces new risks. You still need to vet servers before deploying them. But it's the best standardization attempt we've seen for AI-tool integration, and the industry has voted with adoption.

Getting Started: Practical Next Steps

If You're Building AI Products

  1. Start with the official resources: Check the MCP Registry (preview) for verified servers, then explore github.com/modelcontextprotocol for reference implementations. Browse mcpservers.org and mcp.so for community servers before building custom integrations.

  2. Support MCP in your product: If you're building a SaaS product or API, consider providing an official MCP server. It's a competitive advantage when customers can connect you to any MCP-compatible AI.

  3. Choose your transport wisely: STDIO for local/development, Streamable HTTP for production servers, Stateless Streamable HTTP for cloud-native deployments.

  4. Security from day one: Explicit permissions, comprehensive logging, least privilege, containerization. Don't bolt security on later.

If You're Deploying AI Agents

  1. Evaluate MCP for integration needs: If you're building custom connectors to give AI agents access to internal systems, MCP is worth serious consideration. Start by browsing the MCP Registry to see what's already available.

  2. Review security carefully: Audit any third-party MCP servers. Even with verified publishers in the registry, understand what data they access. Monitor their behavior in production.

  3. Start with read-only: Deploy your first MCP servers with read-only permissions. Prove the value, build confidence, then carefully expand to write operations with appropriate approval workflows.

  4. Integrate with your agent framework: Check if your framework (CrewAI, LangChain, etc.) has MCP adapters. Use them rather than building custom integration code.

For teams building production AI agent systems, the integration layer is often the hardest part. MCP doesn't eliminate that difficulty, but it standardizes it. That's valuable.

Ready to Build Production AI Agents?

At FMKTech, we help organizations build AI agent solutions that actually work in production. MCP is part of our standard integration toolkit because it solves real problems we've encountered repeatedly: connection complexity, security boundaries, platform lock-in.

Whether you're integrating AI with existing systems, building custom MCP servers for proprietary data, or architecting secure multi-agent workflows, we've been there. We know what works, what doesn't, and what security mistakes to avoid.

Want to discuss how MCP and AI agents can transform your operations? Contact us to talk through your use case. We'll help you figure out if MCP makes sense for your architecture and how to implement it without the security nightmares.

The standardization of AI-tool integration is here. The question isn't whether to adopt MCP—it's how quickly you can take advantage of it before your competitors do.

References and Sources

Additional Resources:

Footnotes

  1. Anthropic. (November 2024). Introducing the Model Context Protocol. Retrieved from https://www.anthropic.com/news/model-context-protocol ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10

  2. Model Context Protocol. (2025). Architecture Overview. Retrieved from https://modelcontextprotocol.io/introduction#general-architecture ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9

  3. Model Context Protocol. (2025). Server Features Specification. Retrieved from https://modelcontextprotocol.io/specification/2025-06-18/server/index ↩

  4. Model Context Protocol. (September 2025). MCP Registry Preview. Retrieved from https://blog.modelcontextprotocol.io/posts/2025-09-08-mcp-registry-preview/ ↩

  5. Model Context Protocol GitHub Organization. Official MCP repositories, SDKs, and reference implementations. Retrieved from https://github.com/modelcontextprotocol ↩

  6. Anthropic. (November 2025). Code Execution with MCP. Retrieved from https://www.anthropic.com/engineering/code-execution-with-mcp ↩