What You Will Learn

• Describe MCP fundamentals and Host/Client/Server architecture
• Connect MCP Servers to VS Code and GitHub Copilot
• Publish and discover servers via the MCP Registry
• Implement OAuth 2.1, Dynamic Client Registration, and fine-grained permissions
• Automate database schemas, migrations, and CI/CD with MCP agents
• Design NL Web and MCP UI for conversational, agentic experiences

Study Guide

Welcome to "MCP Dev Days: Day 1 - DevTools" – a comprehensive learning journey into the Model Context Protocol (MCP) and its transformative impact on AI-powered development. Whether you’re a developer, an architect, or a tech leader, this course is designed to guide you from foundational concepts to practical implementation, covering every facet of MCP, its deep integration with modern developer tools, and the broader shift towards intelligent, agent-driven workflows.

In a world where AI is no longer a novelty but a necessity, understanding how to connect Large Language Models (LLMs) with ever-expanding data, tools, and systems is critical. MCP solves the perennial problem of fragmented integrations by acting as a universal adapter, enabling seamless, secure, and scalable connections between AI agents and the digital world. This guide will walk you through MCP’s core architecture, real-world use cases, evolving security requirements, and the vision for an agentic future where natural language becomes the interface to everything.

Let’s dive in and unlock the next wave of AI development, one tool,and protocol,at a time.

Understanding the Model Context Protocol (MCP): The Universal AI Adapter

At its core, the Model Context Protocol (MCP) is a standard that provides context to AI models. It acts as a universal adapter, giving LLMs a consistent way to access data, tools, and resources across environments and applications.

Why MCP matters: Traditionally, integrating AI models with data and tools required custom code for every new connection,an error-prone, repetitive process that limited scalability and flexibility. With MCP, you can connect any LLM to any resource through a standard protocol, accelerating development and opening up entirely new workflows.

Key Features of MCP:

• Standardisation of LLM Interaction: MCP gives every tool and resource a standard interface. Instead of writing custom glue code, everything speaks MCP,making integration repeatable and robust.
• Providing Context and Tools: MCP Servers expose "tools" that agents can invoke (think: open a browser, run a query, fetch a screenshot). They also provide "resources" (API responses, files, prompts) that can be attached to agent requests for richer context.

Example 1: Imagine building a chatbot for customer support. Instead of developing custom APIs for every backend system, you expose those systems as MCP Servers. Now, your AI agent can connect with billing, inventory, and CRM systems using the same protocol.
Example 2: In a developer workflow, an LLM-powered coding agent can use MCP to access code repositories, CI/CD tools, and testing frameworks,all through a single, unified interface.

MCP Architecture: Host, Client, Server

Understanding the three pillars of MCP architecture is essential to leveraging its full potential:

• Host: The AI application (like VS Code or Azure AI Foundry) that orchestrates interactions, manages user sessions, and coordinates multiple MCP clients.
• Client: The bridge between the host and the servers. MCP clients maintain connections and request context or tool executions from MCP servers.
• Server: Lightweight applications,running locally or remotely,that expose tools, resources, and prompts to clients. Servers can represent databases, web APIs, browsers, or custom internal systems.

Example 1: In Visual Studio Code, VS Code acts as the Host, the Copilot extension functions as the MCP Client, and a local Playwright testing tool acts as the MCP Server.
Example 2: In Azure AI Foundry, the platform itself is the Host; it manages agents (Clients) that interact with various MCP Servers representing documentation, databases, or external APIs. Best Practice: Always document your server’s exposed tools and resources clearly. This makes onboarding new agents or clients simple and ensures predictable results.

Deep Integration with Developer Tools: VS Code and GitHub Copilot

The true power of MCP emerges when it’s integrated into the daily tools developers rely on. Let’s explore how MCP transforms workflows in VS Code and with GitHub Copilot.

Enhanced Developer Toolbox

• VS Code implements the full MCP specification, including up-to-date authentication and seamless plugin management.
• Developers can install MCP Servers as extensions directly from VS Code, immediately extending their agent’s capabilities.

Example 1: Using Copilot in VS Code, you can instruct the agent to search GitHub for all repositories matching a theme (like “monkey”), fork a repo, create issues, and assign tasks,all through natural language. The Copilot agent communicates with GitHub via an MCP Server, orchestrating actions that would otherwise require tedious manual steps.
Example 2: For automated testing, you can leverage the Playwright MCP Server. Ask the Copilot agent to debug a specific URL, generate navigation tests, or analyze site structure. The agent uses Playwright as a tool,opening browsers, clicking links, capturing screenshots,without you needing to write glue code. Context Management and Workflow Automation

• The "Add Context" button in VS Code allows you to attach resources (like a GitHub chat message or screenshot) directly to an agent’s prompt. This removes the friction of downloading, formatting, and uploading files.
• Agents can operate with user control: you can require manual approval for every action (for safety), or enable "auto approve" for speed in trusted environments.
• Remote agents, such as those running Copilot in the cloud, can invoke both local and remote MCP Servers,allowing, for example, automatic screenshot inclusion in pull requests.

Example 3: A developer working on a pull request can ask Copilot to summarize code changes, attach relevant files, and request reviews,all handled via MCP Servers plugged into GitHub and local file storage. Tips:

• Use approval settings wisely,manual approval offers more control, while auto-approve streamlines trusted repetitive tasks.
• Leverage context attachments to provide agents with richer, more accurate information for problem-solving.

Expanding MCP's Reach: Azure AI Foundry and Windows OS Integration

MCP is not limited to developer desktops. Its integration with Azure AI Foundry and Windows OS signals a future where AI agents can interact with the entire digital environment,locally and in the cloud.

Azure AI Foundry Integration

• Agents in Azure AI Foundry can call MCP Servers directly, automating tasks like looking up API documentation, querying databases, or managing DevOps pipelines.
• The workflow: set the MCP server URL, define an MCP tool, connect it to the agent, and specify approval policies. This enables rapid onboarding of new tools and centralizes tracing and observability.

Example 1: An agent embedded in Azure AI Foundry can instantly access REST API documentation using an MCP Server, responding to natural language queries about endpoints or usage, and even generating sample code.
Example 2: A DevOps engineer can connect MCP Servers for build systems and testing frameworks, allowing agents to monitor deployments, analyze logs, and diagnose issues. Windows OS Native Integration

• Windows introduces a built-in MCP Registry, allowing users to install, register, and manage MCP services (local or remote). This addresses the pain of manual JSON configuration and API key management.
• Packaged MCP Servers can be cryptographically signed, ensuring they are safe to run natively on Windows. Isolation containers restrict unauthorized access to file systems or networks unless explicitly allowed.
• First-party Windows MCP Servers empower agents to interact with Windows features: app actions, Windows Subsystem for Linux (WSL), system settings, and the file system (including semantic search).

Example 3: Perplexity, an AI agent, uses Windows MCP services to search the local file system for documents about MCP, then composes and sends an email with the results,launching the desktop mail app, all orchestrated through MCP.
Example 4: An agent can manage Windows settings, retrieve or update configurations, and even interact with WSL,all securely controlled through MCP permissions. Best Practice: Always review and manage MCP Server permissions in the Windows MCP Registry to ensure sensitive operations are tightly controlled.

Building a Collaborative Ecosystem: The MCP Registry and Community

MCP’s success relies on a thriving ecosystem, built through collaboration and open standards. The MCP Registry is the foundation for discoverability, security, and interoperability.

Community-Driven Protocol

• The MCP Steering Committee includes Anthropic, OpenAI, Octa, AWS, Microsoft, and others,driving the protocol forward with a shared vision.

MCP Registry: Purpose & Functionality

• The Registry offers a central starting point for MCP Server discovery, addressing fragmentation and reducing the burden on third-party registries or individual server maintainers.
• Publishers define a server.json file with metadata and links to source code (e.g., NPM, PyPI) and remote URLs. They push this data to the registry via CLI.
• Clients (like VS Code Marketplace) pull registry data and enrich it with their own metrics such as download counts or ratings.
• The registry does not store source code, only references and URLs,preserving security and privacy.
• Plans are in place for a meta registry API, enabling single-step publication of MCP Servers to be consumed by multiple directories and registries.
• The registry is open source, built and maintained by the community on GitHub.

Example 1: A developer publishes a new MCP Server for a proprietary database. By submitting a server.json file to the registry, the server becomes discoverable for any client,eliminating the need for repetitive documentation and manual distribution.
Example 2: An enterprise can leverage the registry to pre-approve a set of MCP Servers for all employees, ensuring consistency and compliance. Tips:

• Keep server metadata comprehensive and up-to-date for easier discovery.
• Contribute improvements or bug fixes to the open source MCP Registry to support the ecosystem.

Evolution of User Interaction: The Agentic Future

MCP is paving the way for a new paradigm,one where AI agents provide a "Jarvis-like" experience: natural language, voice, text, gesture, and proactive assistance, all with zero configuration.

Overcoming Historical Integration Challenges

• Previous AI assistants failed to gain traction due to integration complexity,too many services, proprietary APIs, and lack of standard communication. MCP solves this by providing universal, standardized interfaces.

Paradigm Shift: From Web Browsers to Agentic Clients

• The future isn't a browser loading a website; it's an intelligent agent (the "Jarvis client") interacting with MCP Servers representing every tool and resource.
• Specialized clients may use only a few tools; general-purpose clients offer a broad set of capabilities. This unlocks deeply personalized, context-aware user experiences.

NL Web: Bringing LLMs to Every Website

• NL Web (Natural Language Web) is a toolkit/protocol that lets developers expose website data and actions as MCP endpoints. It leverages a site's structured data and content, enabling users to query, search, or interact using everyday language.
• Conversational history and memory are stored locally in the browser, giving users ownership of their data and ensuring privacy.

Example 1: A recipe website exposes its database through MCP and NL Web. Users can ask, "Show me vegan recipes with less than 30 minutes prep time," and the agent returns guaranteed relevant results,no hallucinations.
Example 2: An e-commerce site implements NL Web, allowing users to ask for "laptops under $1,000 with at least 16GB RAM," instantly filtering and displaying matching products through a rich conversational interface. MCP UI Specification

• MCP UI is a sub-specification allowing servers to send rich UI elements (carousels, buttons, forms) to the agent, which renders them in context. This blends natural language with interactive, visual interfaces, enhancing user engagement.

Example 3: An agent helping plan travel can render a carousel of hotel options, each with a "book now" button,moving beyond plain text to actionable, interactive experiences.
Example 4: A shopping assistant presents a list of products with images, prices, and "add to cart" buttons, all within the conversation. Tips:

• Use NL Web to make your site’s data accessible to LLMs,this reduces hallucinations and increases user trust.
• Design MCP UI resources for clarity and actionability,clear buttons and concise text drive better engagement.

Security, Authentication, and Governance in MCP

Security isn’t an afterthought,it’s foundational to MCP’s open ecosystem. Authentication, permissions, and governance must be robust and user-friendly.

Importance of Authentication (Auth)

• Authentication is critical when MCP Servers expose sensitive or restricted data. Without it, anyone could access private information or perform unauthorized actions.

Evolution of MCP Auth Spec

• Early MCP versions required the server to host the OAuth endpoint. Modern MCP supports separation, allowing dedicated authorization servers,a best practice in OAuth 2.1.
• This separation gives publishers flexibility and leverages secure, audited OAuth servers for user login and approval flows.

OAuth 2.1 Best Practices

• MCP follows consolidated OAuth 2.1 standards,eliminating deprecated flows, incorporating features like PKCE (Proof Key for Code Exchange), and focusing on secure, user-friendly experiences.
• The goal: "point and click" authentication for users, with minimal configuration or technical hurdles.

Dynamic Client Registration (DCR)

• DCR allows clients to register with authorization servers dynamically, even if there’s no prior relationship. This is essential for open ecosystems, where clients and servers may encounter each other for the first time.
• Clients discover server information and manage OAuth flows with a single URL, making onboarding seamless.

Enterprise Concerns

• Enterprises demand governance, visibility, auditing, and forensics. They prefer managed authorization profiles, where IT pre-approves MCP Servers and users don’t need to manually approve every connection.
• This balances security and usability, especially for large organizations.

Security Best Practices

• Avoid rolling your own auth: Use established OAuth 2.1 solutions instead of custom authentication logic.
• Fine-grained permissions: Limit what MCP Servers can access; avoid broad or unnecessary scopes.
• Revocation mechanisms: Build in the ability to revoke access instantly if a server or grant is compromised.

Example 1: A finance department uses MCP Servers to access payroll data. Only pre-approved servers are permitted, and every action is logged and auditable.
Example 2: An enterprise enables DCR for internal servers, letting developers experiment safely without waiting for IT to pre-register every new client. Future Security Enhancements

• The MCP community is actively working on solutions for client identity in open ecosystems and plans to release reference architectures (“cookbooks”) for common deployment patterns.

Database Management with AI Agents and MCP

One of MCP’s most compelling applications is in automating database workflows,empowering agents to handle schema design, deployment, and API generation from natural language prompts.

Automating Database Workflows

• MCP Servers for databases (like Gibson AI or Neon DB) let agents actively collaborate on schema design, versioning, testing, and deployment.

Example 1: Gibson AI’s MCP Server enables a developer to generate a fully working database schema with a single prompt,complete with relationship diagrams, keys, and timestamps. The agent can deploy or update schemas, convert old formats, and handle migrations automatically.
Example 2: Neon’s MCP Server (for PostgreSQL) lets Copilot in VS Code create a full-stack Azure Functions REST API to fit a Neon database. The agent writes endpoints, tests queries, and handles database connections,all without manual SQL. Continuous Integration/Deployment (CI/CD) with MCP

• AI agents can monitor code and schema changes, automatically create pull requests on GitHub for schema updates, and trigger CI/CD pipelines for validation.
• Schema changes are first made via chat with the agent, then the AI creates a PR. GitHub Actions validate the changes against a real database branch, ensuring safety and consistency.

Example 3: A developer changes a database schema using conversational prompts. The agent generates the migration code, creates a PR, and GitHub Actions run integration tests,all with minimal manual intervention.
Example 4: The agent provides visual diffs of schema changes in PRs, giving clear insight into what has changed and why. Benefits

• Reduces boilerplate code and manual effort.
• Ensures schema synchronization across staging, development, and production.
• Facilitates safe experimentation with database branching (e.g., temporary test schemas).
• Enables rapid onboarding for new team members,just prompt the agent and let it handle the setup.

Tips:

• Use MCP-enabled agents to maintain strict version control on schema changes and automate testing to catch issues early.
• Leverage agents for documentation generation,ask the agent to produce relationship diagrams or API docs from your database schema.

Key Ideas and Takeaways

Let’s distill the most crucial lessons from the MCP Dev Days: DevTools deep dive:

• MCP is the protocol that brings consistent, scalable context to AI models. Think of it as the USB of AI,plug any model into any tool with minimal friction.
• Microsoft and the broader community (Anthropic, OpenAI, Octa, AWS) are fully committed to MCP’s success, driving open standards and ecosystem collaboration.
• VS Code already supports full MCP integration,start exploring MCP Servers via the Extensions tab to extend your agent’s capabilities instantly.
• Windows OS will offer native MCP support, with a dedicated registry and secure, isolated server packaging.
• The agentic web is the next frontier: instead of static websites, imagine intelligent clients interacting with MCP Servers for every service and resource.
• NL Web makes it easy for any website to expose high-fidelity, conversational interfaces,reducing hallucinations and increasing user trust.
• Modern security is non-negotiable: MCP mandates OAuth 2.1, fine-grained permissions, and revocation capabilities. Dynamic Client Registration is crucial for open, flexible ecosystems.
• Database automation via MCP saves time, reduces errors, and accelerates delivery,giving you more time to focus on what matters.
• This isn’t just a new protocol,it’s the foundation for the next generation of AI applications. You’re at the forefront of what’s possible.

Conclusion: Applying MCP DevTools in Your Work

You’ve now explored the Model Context Protocol from every angle,from its architectural foundations to its practical applications in developer tools, cloud platforms, and beyond. MCP eliminates the chaos of custom integrations, introduces secure, scalable, and user-friendly workflows, and unlocks a future where intelligent agents interact with the digital world as easily as humans do.

The real value of this knowledge comes from application. Start small,integrate an MCP Server into your local development environment or experiment with NL Web on your website. Gradually expand your horizons by connecting more tools, automating database workflows, or contributing to the MCP Registry.

Remember: this is not just about technology. It’s about reclaiming your time, reducing friction, and building AI-driven solutions that work with you. The agentic era is here; your next breakthrough might be just a prompt away.

Frequently Asked Questions

This FAQ section provides clear, practical answers to common questions about the Model Context Protocol (MCP) and its integration with developer tools, as explored in 'MCP Dev Days: Day 1 - DevTools.' Whether you're just starting out or looking for deeper technical insights, this resource addresses topics ranging from MCP fundamentals and architecture to security, real-world applications, and troubleshooting.

What is the Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is a standard protocol designed to provide context to large language models (LLMs).
It acts as a "universal adapter" for AI applications, enabling them to connect seamlessly with various data sources, tools, and resources without requiring custom connections for each integration. This standardisation streamlines the development of AI applications and agents.

How does MCP facilitate interaction between AI applications, data, and tools?

MCP establishes a structured communication method between AI applications, data sources, and tools.
AI applications (hosts) communicate with MCP clients, which obtain context from MCP servers. These servers can connect to databases, code repositories, or testing tools, allowing AI agents to access and invoke functionalities like searching databases or managing code issues. This enriched, automated context makes AI agents more effective and responsive.

What are MCP Servers and how are they used?

MCP Servers are lightweight applications exposing specific functionalities, data, and prompts to MCP clients.
Think of them as "fingers" that the AI agent can "invoke." Examples include a server for databases (like PostgreSQL), a GitHub server for repository interactions, or a Playwright server for automated testing. They provide crucial context, such as API responses or file content, to AI agents automatically, streamlining workflows and reducing manual effort.

How can developers get started with MCP in Visual Studio Code?

Developers can install MCP servers directly from the Extensions tab in Visual Studio Code.
A curated list helps users choose popular MCP servers, and developers can also submit their own. Once installed, these servers extend an agent's abilities,such as searching repositories or running automated tests via natural language. Configuration is flexible, managed via an mcp.json file or within the chat window.

How is MCP being integrated into broader platforms like Windows and Azure AI Foundry?

MCP is being integrated natively into platforms like Windows and Azure AI Foundry.
On Windows, MCP offers built-in registration, a set of default servers, and deep system integration. Azure AI Foundry's Agent Service uses MCP to allow agents to call tools directly, enabling tasks like accessing Azure REST API documentation through natural language, with human oversight where needed.

What is the MCP Registry and how does it address fragmentation in the ecosystem?

The MCP Registry is a central, open-source service for discovering and managing MCP servers.
Before its introduction, third-party registries often had fragmented data. The MCP Registry provides a single, authoritative source for server metadata and installation instructions, simplifying server publishing and ensuring consistent, reliable integrations across platforms like VS Code and GitHub.

How does MCP address security and authentication concerns?

MCP prioritises security, using OAUTH 2.1 for authentication and separating the authorization server from the MCP server.
This "point and click" approach lets users connect servers easily, while allowing enterprises to rely on their existing OAUTH infrastructure. Features like Dynamic Client Registration (DCR) and Protected Resource Metadata are being explored for secure, flexible deployments and robust access control.

How is MCP changing the future of user interaction with AI?

MCP is enabling a shift to more natural, proactive user interactions with AI agents.
Instead of relying on traditional web interfaces, users interact with "Jarvis clients" that connect to MCP servers for real-time, context-rich actions. This eliminates the old "integration problem," letting any MCP client communicate with any server, and paves the way for richer graphical and conversational interfaces.

What is the primary purpose of MCP as described by James Montemagno?

MCP's main purpose is to provide context to AI models in a unified, standardised way.
By acting as a universal adapter, MCP streamlines how large language models connect with data, tools, and resources, eliminating the need for custom integrations for every application.

What are the three core components of the MCP architecture?

The three core components are:
1. Host: The AI application managing clients and coordinating actions (e.g., VS Code).
2. Clients: Components that connect to MCP servers to obtain context.
3. Servers: Lightweight applications providing context or functionality to clients.

How can MCP Servers extend the capabilities of an AI agent?

MCP Servers expose tools and functionalities that agents can invoke to perform tasks.
These can include retrieving data, submitting forms, opening browsers, making clicks, or attaching resources like API responses or screenshots,broadening what AI agents can achieve in different environments.

What does "context" mean in the context of AI agents and MCP Servers?

Context refers to all the relevant information provided to the AI agent or model to inform its understanding and actions.
This can be user input, attached screenshots, API responses, or files,often enriched automatically by MCP Servers, reducing manual requests and enabling more intelligent responses.

What is the function of the MCP Registry in Windows, and what problems does it solve?

The MCP Registry in Windows allows users to install and register MCP services, whether local or remote.
It solves challenges like managing JSON configurations, handling permissions, and securely storing API keys, while also addressing security and discoverability concerns as users adopt new technology.

What is the key feature of NL Web that brings LLMs into every website?

NL Web leverages a site's structured data and exposes it via an MCP endpoint.
This enables users to interact with website content through natural language, providing relevant, hallucination-free search and interaction experiences directly tied to the site's data.

How does MCP enable a more seamless user experience compared to traditional AI assistant integrations?

MCP standardises communication between AI clients and servers, removing the need for one-off integrations.
This allows service providers to manage their integrations once, making a wide range of tools accessible through a single AI client and reducing complexity for users and developers alike.

What is "sampling" in MCP, and how does it facilitate interaction?

Sampling allows an MCP Server to request additional input from the user or leverage the client's LLM for content generation.
This is helpful when the server needs more information to complete a task, enabling richer, more dynamic interactions and responses tailored to the user's needs.

Why is authentication crucial for MCP, especially with restricted data?

Authentication governs access to sensitive data and prevents unauthorised actions.
Without robust authentication, users' private data or system resources could be exposed or manipulated. This is especially critical in enterprise settings or agent-to-agent communications where security is paramount.

What is the significance of OAUTH 2.1 in the MCP authentication process?

OAUTH 2.1 consolidates best practices for secure, modern authentication in the MCP ecosystem.
It separates authorization from the MCP Server, allowing for more flexible, secure deployments and integration with enterprise-grade identity providers, improving user and administrator control over access.

What is the paradigm shift from "web browsers and websites" to "Jarvis clients and MCP Servers"?

This shift moves user interaction from static web browsing to dynamic, conversational engagement with AI agents.
MCP enables agents to stitch together services and data from multiple MCP Servers, addressing the historical integration problem and making complex, cross-system tasks accessible through natural language or gestures.

How do custom MCP Servers support enterprise needs?

Custom MCP Servers offer enterprises fine-grained control over what context is shared with AI agents.
Enterprises can tailor which data and actions are exposed, meeting security and compliance requirements. While building custom servers requires initial investment, it ensures sensitive data is handled according to corporate policies, unlike relying solely on public servers.

What is the role of "memory and conversational history" in NL Web and MCP?

Memory and conversational history allow users to retain ownership of their interactions and data.
Instead of storing data solely on central servers, users control their own conversational history, leading to greater privacy and flexibility,contrasting traditional models where data is often locked into proprietary platforms.

How are MCP Servers used in database management and developer workflows?

MCP Servers can automate common tasks like schema design, migration, and API generation for databases.
For example, a developer can ask an AI agent to generate a new database schema or migrate data, with the MCP Server handling the heavy lifting. This streamlines workflows, reduces manual coding, and helps maintain consistency across projects.

What security challenges does MCP face, and how are they being addressed?

Key challenges include secure authentication, dynamic client registration, and enterprise deployment.
The MCP community is adopting OAUTH 2.1, exploring Dynamic Client Registration, and working closely with enterprise security teams to ensure servers and integrations meet stringent requirements for access control and auditability.

How does communication flow between MCP Clients and Servers?

MCP Clients connect to MCP Servers to request context or invoke tools, using a structured protocol.
The client usually initiates requests, and servers respond with data or actions. In some cases, servers can request additional information, such as through sampling, leading to a two-way conversation that enhances agent intelligence.

How is MCP different from traditional APIs or plugins?

Unlike traditional APIs or plugins, MCP provides a standardised, context-rich protocol for AI agents to interact with multiple tools and data sources.
This removes the need for each tool to be individually integrated with every host application, radically simplifying maintenance and expanding interoperability.

How do I install and manage MCP Servers in my environment?

You can install MCP Servers from official registries like the MCP Registry or directly within supported applications (e.g., VS Code).
Management typically involves registering the server, setting up permissions, and configuring access tokens or API keys as needed. Documentation is usually provided by each server developer for setup and usage.

Can non-developers benefit from MCP?

Yes, MCP is designed to simplify AI integration for both technical and non-technical users.
Business users can leverage MCP-powered agents within applications to automate tasks, retrieve insights, or interact with data using natural language,without needing to write code.

What should I do if an MCP Server isn't working as expected?

First, check the server's documentation and ensure it's properly installed and configured.
Verify network connectivity, permissions, and API keys. If issues persist, consult the MCP Registry or community forums for troubleshooting tips, or reach out to the server maintainer for support.

What are typical challenges when integrating MCP into existing workflows?

Common challenges include aligning security policies, managing authentication, and mapping existing workflows to MCP's model.
It's important to plan integration steps, involve security and IT teams early, and start with pilot projects to ensure a smooth transition. Documentation and community support can help resolve technical hurdles.

What are some real-world examples of MCP Servers?

Examples include:
- Database servers (e.g., PostgreSQL) for schema and query management
- GitHub servers for repository and issue tracking
- Playwright servers for automated UI testing
- NL Web endpoints for conversational website interaction
These servers enable AI agents to perform a range of tasks, such as generating migration scripts or managing code reviews, with minimal manual effort.

How does MCP work with enterprise identity systems like Entra ID?

MCP integrates with enterprise identity providers like Entra ID via OAUTH flows.
This allows organisations to enforce access policies, audit usage, and manage permissions centrally, meeting compliance and governance requirements.

What are the benefits of using MCP for developers?

Developers benefit from simplified integrations, reusable tools, and enhanced agent capabilities.
MCP reduces boilerplate code, standardises how agents access external resources, and accelerates the development of intelligent, context-aware applications.

How does MCP contribute to the development of conversational interfaces?

MCP enables agents to interact with data and tools through natural language prompts and responses.
This makes it easier to build chatbots, virtual assistants, or voice interfaces that can access structured data, perform actions, and provide insightful responses,bridging the gap between conversational AI and practical automation.

What security measures should be considered when deploying MCP Servers?

Key measures include enforcing OAUTH-based authentication, auditing access logs, and restricting server functionalities to necessary actions.
Enterprises should also review server source code for vulnerabilities, use isolated environments for testing, and regularly update servers to patch security issues.

How are MCP Servers updated and maintained?

MCP Servers are typically maintained by their developers and updated via the MCP Registry or platform-specific extension managers.
Server maintainers provide release notes and upgrade instructions. Users should stay informed of updates to benefit from new features and security patches.

Can MCP facilitate collaboration between multiple AI agents?

Yes, MCP provides the foundation for agents to communicate and collaborate by sharing context and invoking each other's tools.
This enables complex workflows, such as one agent preparing data for another or coordinating multi-step business processes across different platforms.

How does MCP protect user privacy?

MCP adopts best practices for data minimisation, user consent, and secure authentication.
Users have control over which servers and tools they connect, and enterprises can enforce privacy policies at the server and registry levels.

How is MCP used in automated testing scenarios?

MCP Servers like Playwright expose automated testing capabilities to AI agents.
Agents can generate, execute, and analyse test scripts using natural language, helping developers automate repetitive QA tasks and identify issues faster.

What future developments are expected for MCP?

Upcoming features include deeper platform integration, expanded server registries, improved GUI interfaces, and advanced security options.
The MCP community is actively working on new protocols for richer agent interactions, dynamic registration, and more seamless user experiences across devices and applications.