How MCP is Revolutionising Agentic AI

AI assistants have become powerful, but it’s data is isolated. They can access what they have been trained on, yet are not easily integrated with your current business data, customer information or internal system records. Every new data source requires a custom connector, with scaling becoming problematic. This was the case until the release of the Model Context Protocol (MCP), which aims to solve these problems.

MCP can be described as the USB-C port for AI data source connections. Just as USB-C has standardized how devices connect across the tech ecosystem, MCP has standardized how AI systems are able to be connected to external data sources and gain the ability to use them as tools.

In this guide we’ll explore what MCP is, why it matters to you, how it works, and how you developers and organizations can start building with this protocol today.

What has MCP done to the current AI landscape

MCP was released in November 2024 by Anthropic, and has rapidly become the foundation of modern AI integration. Companies such as OpenAI, Google, Deepmind and Microsoft have all adopted this protocol, and estimations are that by the end of 2025 90% of organizations will be using MCP in some form as part of their wider AI ML Development efforts.

Before MCP connecting models to external data-sources was tedious with every integration requiring a custom solution. Your chatbot needed a custom connector to Slack and your code assistant needed a custom connector to GitHub. This resulted in AI systems that remained isolated from one another, were expensive and hard to maintain, and did not scale efficiently, especially for teams working on AI ML Development projects.

This methodology created several critical problems.

• Information silos: AI systems couldn’t access data living in legacy systems, databases, or proprietary tools.
• Custom integration tax: Each new data source required dedicated developer time and maintenance, with single API changes able to break the system.
• Scalability challenges: As organizations grew in size, maintaining dozens of custom connectors became impractical for most organizations
• Vendor lock-in: Integrations were tied to specific AI platforms (OpenAI’s function-calling, ChatGPT plugins, etc.), making it hard to use other providers which did not offer those integrations
• Security concerns: With no standardization against auth, logging, and encryption Ad-hoc integrations created inconsistent security practices and compliance headaches.

How MCP fixes these issue

At its core, MCP is an open source protocol that has standardized how AI systems communicate with external data sources and tools. It allows AI systems such as Claude or OpenAI to securely request data and execute pre-determined commands within your systems.

Instead of requiring custom integrations for each AI platform and data source, organisations can now build a single MCP server that exposes their data or tools, with any MCP-compatible AI client able to connect to them. The architecture is also elegantly simple.

• MCP Servers: Services that expose your data, tools, and capabilities (e.g., a server that connects to your database, file system, or business tools)
• MCP Clients: An AI applications component that connects to servers to access data and tools for the MCP host to execute.
• MCP host: The AI application that coordinates and manages one or more MCP clients.
• Transport layer: JSON-RPC 2.0 messages sent over standard protocols (stdio for local, HTTP+SSE for remote)

It’s also important to note what clients and servers can offer each other, as seen in the above diagram; this can be tools, resources or prompts. These are defined by Anthropic as follows.

• Tools: Executable functions that AI applications can invoke to perform actions (e.g., file operations, API calls, database queries)
• Resources: Data sources that provide contextual information to AI applications (e.g., file contents, database records, API responses)
• Prompts: Reusable templates that help structure interactions with language models (e.g., system prompts, few-shot examples)

How MCP works – Practical examples

We’ve discussed what MCP is, what problems it can solve and how the architecture is set up. Now, let’s see some real-world examples of how you or any organisation could be implementing MCP servers today.

Let’s take the example of an AI model being used as a financial advisor for a company, retrieving stats from their internal databases.

User types query to AI agent (Claude): “What was our revenue last quarter?”

• MCP host (Claude) sends query to client: The AI application recognizes it needs external data, so it goes through the process of the MCP protocol.
• Client discovers available tools: Via MCP, the client checks what resources the finance MCP server can access, the MCP server may have a specific tool configured by the company or user that retrieves x data related to financials.
• Client makes a request: It sends a standardized MCP request to retrieve the revenue figure
• Server processes and responds: The MCP server queries the finance database and returns the actual, up-to-date number.

Without MCP, each step would require custom code. With MCP, it’s all standardized. You can swap out the finance system, change AI providers, or add new tools—and the connections keep working because they all speak the same language.

Real-World Adoption: Who’s Using MCP?

As MCP is a relatively new protocol, there is still time before widespread adoption is supported by all AI models. We will outline some of the key players who have already adopted the protocol, allowing you to start building MCP servers, creating tools and expediting your ability to seize the boost in productivity, knowledge management, resource management and all the other benefits gained by agentic AI.

1. OpenAI: Integrated MCP into ChatGPT desktop app, its Agents SDK, and the Responses API (March 2025)
2. Google DeepMind: Committed to MCP support in upcoming Gemini models and infrastructure (April 2025)
3. Microsoft: Made MCP generally available in Copilot Studio (May 2025) and is integrating it into Windows-level security architecture.e
4. Development tools: Zed, Replit, Sourcegraph, and Codeium have adopted MCP to give AI assistants real-time access to code and project context.
5. Early adopters: Block and Apollo have integrated MCP into their products
6. Enterprise: Predictions suggest 90% of organisations will use MCP by the end of 2025

This rapid adoption has solidified MCP as the standard for AI integrations.

Building MCP Servers: Getting Started

Anthropic provides SDKs in Python, Typescript, C#, and Java, providing straightforward documentation on how to build MCP servers. The organisation also publishes pre-built references for common enterprise systems such as

• Google Drive
• Slack
• GitHub
• Git
• PostgreSQL
• Puppeteer (browser automation)
• Stripe (payment processing)

For people or organisations with unique use cases, you can now build your own MCP server. The basic workflow is as follows:

• Choose your SDK (TypeScript for most web applications)
• Define what resources or tools your server exposes
• Implement the logic to handle requests
• Deploy locally (for testing) or remotely (for production)
• Connect via any MCP-compatible AI client

Because MCP is standardised, you don’t need to optimise for specific AI platforms. Build once, and you can connect everywhere.

Security: The Critical Consideration

With great power comes great responsibility. MCP enables AI systems to access sensitive data and execute commands, so ensuring your system’s security is paramount.

The protocol itself provides built-in security mechanisms, but implementation matters. Recent MCP specification updates (June 2025) clarified authorisation using OAuth, introduced resource Indicators to prevent token leakage, and emphasized security best practices.

Key security principles for MCP deployment:

• OAuth-based authorization: MCP servers are now classified as OAuth Resource Servers, enabling secure token handling implementation.
• User consent flows: Users should explicitly approve which tools each client can access
• Least privilege: Expose only the minimal resources and capabilities needed for each use case
• Audit logging: Track all client-server interactions for compliance and incident response
• Input validation: Sanitize all inputs to prevent prompt injection and command injection attacks

Organizations or individuals deploying MCP should also be aware of emerging threat vectors like cross-prompt injection (XPIA), tool poisoning (malicious MCP servers), and credential leakage. These require thoughtful architectural decisions and ongoing security monitoring.

Practical implementation of MCP servers

If you’re ready to take the journey to transform your workflows with agentic AI using MCP, below is an outline of steps to consider.

Step 1: Understand Your Use Case

• What systems do you want AI to access? (databases, APIs, file systems, business tools)
• What actions do you want AI to perform? (read data, write data, trigger workflows)
• Which AI platforms are important to your organization? (Claude, ChatGPT, etc.)
• What are your security constraints? (data sensitivity, compliance requirements)

Step 2: Choose Your MCP Architecture

• Use pre-built servers: If you use common tools (GitHub, Slack, Google Drive), start with Anthropic’s reference servers.
• Build a custom server: For proprietary systems or specialised use cases, invest in building custom servers.
• Hybrid approach: Combine pre-built and custom servers for your tech stack.

Step 3: Build Your First MCP Server.

• Select an SDK.
• Define resources and tools your server will expose.
• Implement authentication and authorisation using OAuth.
• Test locally using platforms like Claude Desktop or Claude for Work.
• Add input validation and security measures.

Step 4: Deploy your MCP server

• Choose a deployment model (local for testing, remote for production).
• Implement monitoring, logging, and alerting.
• Set up user consent flows for tool access.
• Document your server’s capabilities and security model.

Step 5: Connect Your AI Clients (Ongoing)

• Integrate MCP servers into your AI applications (Claude, ChatGPT, Copilot Studio, etc.)
• Test end-to-end workflows.
• Gather user feedback and iterate.
• Monitor for security issues and performance.

How Solacecode can help you implement MCP

The organisations that build MCP integrations today will have a meaningful advantage. They’ll have AI systems that can access real data, execute actions, and provide genuine value—not just generate plausible-sounding text.

If you’re ready to build with MCP—whether that’s connecting existing systems, creating custom MCP servers, or integrating AI into enterprise workflows—let’s talk. At SolaceCode, we’ve helped multiple organisations implement MCP ecosystems that unlock AI’s full potential while maintaining security and compliance.

Book a free consultation where we’ll assess your current systems, identify high-value MCP opportunities, and outline an implementation roadmap. Whether you’re an early adopter or planning your MCP strategy for 2026, we can help.