If you’ve been following AI developments recently, you’ve probably heard the term MCP popping up everywhere. But what is MCP exactly, and why are companies from OpenAI to Microsoft racing to adopt it?
The Model Context Protocol has quietly become the most important standard in AI development since Anthropic launched it in November 2024. In just one year, it has transformed from an open-source experiment into the de facto standard for connecting AI models to external tools and data.
This guide breaks down everything you need to know about MCP, from basic concepts to practical applications.
What is MCP in AI?
The Model Context Protocol is an open standard that defines how AI systems connect with external data sources, tools, and workflows. Think of it as a universal language that allows large language models to interact with the software and services you already use.
Before MCP existed, every connection between an AI system and a business application had to be custom-built. Developers needed one integration for Slack, another for Google Drive, and yet another for their database. Each integration introduced variation, delay, and risk.
MCP changes this by providing a standardised framework. According to Moody’s analysis of the protocol, it serves as “the foundational layer for enterprise AI connectivity,” particularly valuable in regulated industries that require clear audit trails and real-time controls.
How Does the Model Context Protocol Actually Work?
MCP uses a straightforward client-server architecture with three key components.
MCP hosts are programs like Claude Desktop or ChatGPT that want to access external data or tools. They’re the AI applications you interact with directly.
MCP clients live within the host application and manage connections to MCP servers. They handle the communication and ensure requests reach the right destination.
MCP servers are external programs that expose tools, resources, and prompts via a standardised API. When you install an MCP server for GitHub, for instance, it tells the AI exactly what actions are available, such as creating issues, listing repositories, or searching code.
The communication happens through JSON-RPC 2.0, a lightweight protocol that makes interactions predictable and auditable. When an AI agent needs to perform an operation, it connects through the MCP server using OAuth 2.0 authentication, ensuring security at every step.
| Component | Role | Example |
|---|---|---|
| MCP Host | Runs the AI model and user interface | Claude Desktop, ChatGPT |
| MCP Client | Manages server connections | Built into the host application |
| MCP Server | Exposes tools and data to the AI | GitHub server, Slack server |
Why Do LLMs Need MCP?
Large language models are exceptional at generating text and insights, but they generally cannot act on live data. A customer service chatbot might explain how to reset a password brilliantly, yet it cannot actually perform that action because it lacks secure, structured access to internal systems.
MCP solves this problem by giving AI systems a way to request and perform actions within approved security boundaries. It allows AI to move from being a passive assistant to an active helper.
As highlighted in Anthropic’s engineering blog, the protocol lets models safely run code on behalf of users, enabling direct execution of tasks within secure, sandboxed environments. In an enterprise setting, this means an AI assistant can pull the latest sales data or summarise a client report without exposing sensitive files.
Is MCP the Same as an API?
This is one of the most common questions, and the answer is nuanced. MCP and APIs serve different purposes and work together rather than competing.
APIs are like custom-built bridges between systems. Each one has unique endpoints, data formats, and authentication methods. They’re general-purpose and widely used across industries, but every API is different.
MCP is more like a universal USB-C port for AI applications. It provides a standardised, versatile connection point that lets AI agents plug into a wide range of external services without needing a new adapter for each one.
Here’s a practical distinction from IBM’s analysis: with APIs, you tell the system how to do something step by step. With MCP, you tell the AI agent what you want to achieve, and it figures out the how.
MCP often acts as a translator for existing APIs. A GitHub MCP server might offer a simple command like “list repositories,” but behind the scenes, it uses GitHub’s real API to perform the task. The AI doesn’t need to understand the complexities of the underlying API.
What Problems Does MCP Solve for AI Developers?
Before MCP, building AI integrations was a fragmented, time-consuming process.
Redundant development work meant teams built the same connectors repeatedly. Every project that needed Slack integration required someone to write that code from scratch.
Inconsistent security arose because each custom integration handled authentication and permissions differently, creating potential vulnerabilities.
Maintenance burden grew exponentially as APIs changed and integrations broke, requiring constant updates across multiple codebases.
Limited discoverability meant AI agents couldn’t automatically learn about new tools or capabilities without manual programming.
MCP addresses each of these issues. The November 2025 specification update, marking the protocol’s first anniversary, introduced support for task-based workflows, simplified authorisation flows, and enhanced security features designed specifically for enterprise deployment.
The MCP Registry now houses nearly two thousand servers, representing 407% growth since its launch in September 2025. This explosion demonstrates how quickly the developer community has embraced the standard.
Do I Need MCP for My AI Project?
The honest answer depends on your specific requirements.
You likely need MCP if:
- Your AI application needs to access multiple external services
- You’re building enterprise solutions that require audit trails
- You want AI agents to take actions, not just provide information
- You’re concerned about maintenance overhead from custom integrations
You might not need MCP if:
- You’re building a simple chatbot with no external integrations
- Your use case involves only a single, well-documented API
- You’re in an experimental phase and don’t yet know your integration needs
For most production AI applications in 2026, MCP has become the sensible default choice. Major platforms including Claude, ChatGPT, and various enterprise tools now support it natively.
How Does MCP Make AI Tools Work Together?
One of MCP’s most powerful features is dynamic discovery. AI agents can query MCP servers at runtime to discover available tools and data, adapting automatically to new or updated capabilities without redeployment.
This creates what Docker has called a shift from “What do I need to configure?” to “What can I empower agents to do?” Their MCP Gateway includes features like Smart Search that let agents find, add, and configure MCP servers dynamically during a session.
Imagine you’re using an AI coding assistant. Instead of pre-configuring every possible tool, you simply ask it to search for files. The agent queries the catalogue, finds a relevant MCP server, pulls the image, spins it up, and proceeds with your request, all without you leaving the conversation.
The MCP Apps Extension, proposed in November 2025, takes this further by enabling MCP servers to deliver interactive user interfaces. This means AI agents can now present visual information or gather complex user input through standardised patterns.
Is MCP Secure?
Security was a foundational concern in MCP’s design, though like any powerful tool, it requires careful implementation.
Built-in security features include:
- OAuth 2.0 authentication for server connections
- Permission inheritance that mirrors user access rights
- Audit logs for every interaction
- Sandboxed execution environments
The December 2025 specification update added several enterprise-focused security enhancements. According to Artificial Intelligence News, these changes help organisations move AI agents from pilot projects to production deployments with confidence.
However, security researchers have identified potential vulnerabilities. Unit 42’s analysis examined how malicious MCP servers could exploit the sampling feature for attacks including resource theft, conversation hijacking, and covert tool invocation. This underscores the importance of only connecting to trusted MCP servers and implementing proper governance.
Okta’s Cross App Access now extends MCP to provide enterprise-grade security for AI agent interactions, demonstrating how the ecosystem is maturing around security needs.
Can MCP Connect an AI Agent to My Own Databases?
Yes, and this is one of the most valuable use cases.
MCP servers can be built to expose database operations while respecting existing access controls. When properly configured, an AI agent can only perform operations that the user is authorised to do. If a role restricts accessing certain tables, those same restrictions apply to AI operations performed on the user’s behalf.
GreptimeDB offers an MCP server specifically designed for secure database interaction, supporting real-time metrics and analytics while maintaining strict access controls. ClickHouse provides similar capabilities for analytical workloads.
The key principle is permission inheritance, not permission expansion. The AI gains the ability to work more efficiently with your data, but never sees more than you’re already allowed to access.
How is MCP Different from Plugins or Extensions?
Browser extensions and AI plugins typically require explicit installation, manual configuration, and often work only within a specific platform. They’re point solutions designed for particular use cases.
MCP provides a standardised protocol that works across multiple hosts and enables dynamic capability discovery. Rather than installing a separate plugin for every service, you connect to MCP servers that expose consistent interfaces.
The difference becomes clear in practice. A plugin might add Slack integration to one specific AI tool. An MCP server for Slack works with any MCP-compatible host, and the AI can discover and use its capabilities automatically.
Will MCP Make AI Models More Accurate?
MCP doesn’t directly improve model accuracy in terms of the underlying language understanding. However, it significantly improves the practical accuracy of AI responses by providing access to real-time, authoritative data.
An AI answering questions about your company’s sales figures will give more accurate responses when it can query your actual database rather than relying on training data that might be months or years old. This is particularly valuable in rapidly changing domains.
Retrieval-Augmented Generation (RAG) improves knowledge by finding relevant documents. MCP expands capability by enabling action. The combination of both approaches produces AI systems that know more and can do more.
Does MCP Help with Hallucinations?
MCP helps reduce hallucinations in specific ways.
When an AI can verify information against authoritative sources in real time, it’s less likely to fabricate details. Instead of guessing about a customer’s account status, an MCP-connected agent can check the actual record and report accurately.
The protocol also supports what Anthropic calls “grounded responses,” where the AI explicitly references the tools and data sources it consulted. This transparency makes it easier to verify claims and catch errors.
However, MCP isn’t a complete solution to hallucinations. The AI can still misinterpret retrieved information or make mistakes in reasoning. It’s one important tool in building more reliable AI systems rather than a silver bullet.
Is MCP Expensive to Use?
The Model Context Protocol itself is open-source and free to use. Anthropic developed it as an open standard specifically to encourage broad adoption.
Costs come from adjacent factors:
- Hosting MCP servers requires compute resources
- API calls to underlying services may incur charges
- Development time to build custom MCP servers if needed
- Enterprise features from commercial providers may require licences
Many pre-built MCP servers are available at no cost through the community registry. For organisations already using services like GitHub, Slack, or Google Drive, adding MCP integration typically doesn’t increase underlying service costs.
Can Beginners Use MCP Without Coding?
The ecosystem has matured significantly since launch, making MCP increasingly accessible to non-developers.
Claude Desktop provides a user-friendly interface for connecting to MCP servers without writing code. Users can add servers through configuration files and immediately start using their capabilities.
Tools like Toolbase are emerging to handle key management and proxying for local setups, smoothing out technical complexity. The Atlassian Rovo MCP Server brings Jira and Confluence integration to ChatGPT users with minimal setup.
That said, building custom MCP servers still requires programming knowledge. If your needs aren’t met by existing servers, you’ll need development resources or will need to commission custom work.
What Tools Support MCP Right Now?
The list of MCP-compatible tools has grown dramatically throughout 2025.
AI platforms:
- Claude Desktop (Anthropic)
- ChatGPT (OpenAI)
- Cursor (coding editor)
- Amazon Q Developer
- Microsoft Copilot
Enterprise applications:
- Notion
- Slack
- GitHub
- Stripe
- Jira and Confluence
- Hugging Face
- Postman
Infrastructure:
- Docker (MCP Gateway with 270+ curated servers)
- AWS services
- Microsoft Sentinel
- Terraform
The Docker MCP Catalog now includes over 270 curated servers covering everything from file systems to Google Maps to enterprise data platforms. The community registry continues to grow rapidly.
How Does MCP Handle Privacy?
Privacy protection in MCP operates at multiple levels.
Authentication ensures only authorised users and applications can access MCP servers. OAuth 2.0 provides standardised, secure credential handling.
Permission controls mean users only access data they’re already authorised to see. MCP doesn’t create new access paths; it provides a more efficient way to use existing permissions.
Data residency depends on where MCP servers are hosted. Organisations can run servers on-premises or in specific geographic regions to comply with data protection requirements.
Audit logging captures who accessed what and when, supporting compliance with regulations like GDPR and HIPAA.
For healthcare applications, First Databank explained that MCP creates “an AI-native layer of context awareness and governance that lets organisations control what an AI can see and do, ensuring every recommendation is explainable, traceable and aligned with clinical standards.”
Is MCP Open-Source?
Yes, MCP is fully open-source. Anthropic released it as an open standard specifically to enable broad adoption and community contribution.
The specification, reference implementations, and many servers are available on GitHub. The community actively contributes new servers, proposes specification improvements through the SEP (Specification Enhancement Proposals) process, and helps resolve issues.
The governance structure ensures community leaders and Anthropic maintainers work together on the protocol’s evolution. Working and Interest Groups allow contributors to shape specific aspects of the standard.
What Are the Benefits of MCP for Non-Technical Users?
For people who don’t write code, MCP offers several practical advantages.
Unified experience means interacting with multiple services through a single AI interface rather than switching between applications. You can ask your AI assistant to check your calendar, send an email, and update a project task in one conversation.
Reduced learning curve comes from not needing to understand how each service works. The AI handles the complexity of interacting with different systems.
Consistency across different tools and platforms makes AI assistance more predictable and reliable.
Time savings accumulate quickly when routine tasks that previously required multiple applications and manual steps can be handled through natural conversation.
Walmart’s recent experience illustrates this. After consolidating dozens of narrow AI agents into four “super agents” using MCP, they achieved more scalable and efficient AI infrastructure across customers, employees, engineers, and suppliers.
Can MCP Automate Tasks on My Computer?
Yes, though with appropriate safeguards.
Local MCP servers can interact with your file system, run applications, and perform other desktop operations. Cursor, the popular coding editor, demonstrates this well: add a file system MCP server and your AI assistant can read, write, and organise files directly.
Security considerations remain important:
- Only install MCP servers from trusted sources
- Review the permissions each server requests
- Be cautious about servers that require broad system access
- Keep servers updated to receive security patches
The MCP Apps Extension adds an additional layer by enabling interactive user interfaces for permission management and confirmation of sensitive operations.
Does MCP Work with ChatGPT?
As of late 2025, yes. OpenAI has integrated MCP support across ChatGPT and their developer platform.
The Atlassian Rovo MCP Connector for ChatGPT is among the first major third-party integrations, allowing ChatGPT users to summarise Jira work items, create issues directly within the chat interface, and enrich Jira content with context from multiple sources.
OpenAI’s Srinivas Narayanan, CTO of B2B Applications, stated that MCP has become “a key part of how we build at OpenAI, integrated across ChatGPT and our developer platform.”
Microsoft Sentinel’s MCP server also supports ChatGPT connection through secured OAuth authentication, bringing security analytics capabilities to ChatGPT users.
How Does MCP Reduce Context Limits in LLMs?
Context limits are a fundamental constraint in large language models. There’s only so much information you can include in a single prompt before hitting the model’s limit.
MCP helps address this in several ways.
On-demand retrieval means the AI fetches information when needed rather than requiring everything upfront. Instead of including your entire customer database in the context, the AI queries specific records as relevant.
Dynamic tool selection keeps tool definitions out of the context until they’re needed. The Docker MCP Gateway distinguishes between tools that are available and ones actively loaded into the context window.
Code mode composition allows AI agents to write scripts that call multiple tools, reducing the need to include detailed tool documentation in every request. Anthropic’s building more efficient agents post details how this approach can eliminate hundreds of thousands of tokens from each turn.
Is MCP Going to Replace APIs and Integrations?
No. MCP complements APIs rather than replacing them.
APIs remain the fundamental building blocks of software integration. They define how systems communicate at a technical level. MCP sits as an intelligent layer on top, allowing AI agents to use APIs more effectively.
Think of it this way: APIs are the plumbing that moves data between systems. MCP is the smart thermostat that decides when and how to use that plumbing based on what you’re trying to accomplish.
Most MCP servers are actually wrappers around existing APIs. They translate between the standardised MCP protocol and whatever unique API the underlying service provides.
Can I Build My Own MCP Server?
Absolutely. Building custom MCP servers is one of the protocol’s most powerful features for developers.
Getting started requires:
- Familiarity with your chosen programming language (Python, TypeScript, and Go have strong SDK support)
- Understanding of the service you want to expose
- Basic knowledge of JSON-RPC 2.0
The official documentation provides guides for building servers in multiple languages. Tools like Mintlify and Stainless can generate MCP-ready servers from existing API documentation.
A typical server includes:
- Tool definitions describing available actions
- Resource definitions for read-only data access
- Authentication handling
- Error management
Once built, your server can be used by any MCP-compatible client, from Claude Desktop to custom applications.
Why Are Companies Adopting MCP?
Enterprise adoption has accelerated dramatically in 2025, driven by several factors.
Standardisation reduces costs. Instead of building and maintaining separate integrations for every AI tool, companies invest once in MCP servers that work everywhere.
Security and compliance improve. MCP’s audit trails, permission controls, and standardised authentication satisfy enterprise security requirements.
AI effectiveness increases. When AI agents can access real business data and take meaningful actions, they deliver substantially more value.
Vendor flexibility grows. MCP prevents lock-in to any single AI platform. Companies can switch between Claude, ChatGPT, or other providers while keeping their integrations intact.
GitHub, Notion, Stripe, and Hugging Face have all built official MCP servers. Microsoft has added native MCP support to Windows 11. Block (formerly Square) uses MCP at the heart of products like Square AI and their internal systems.
How Do MCP Prompts Differ from Normal LLM Prompts?
MCP doesn’t change how you write prompts to AI models. Instead, it changes what the AI can do with those prompts.
A normal prompt might ask: “What’s the weather in Edinburgh?”
With MCP, the AI can actually check. It discovers the weather MCP server, calls the appropriate tool, receives current conditions, and incorporates that real data into its response.
Prompt templates are one MCP primitive that does affect prompting. These are predefined prompt structures that guide AI queries for specific use cases. A customer service prompt template might include standard information retrieval steps, ensuring consistent handling across different queries.
Will MCP Make AI Agents More Reliable?
Reliability improvements come from several MCP characteristics.
Structured interactions follow predictable patterns. The protocol defines exactly how requests and responses should be formatted, reducing unexpected behaviour.
Error handling is standardised. When something goes wrong, MCP provides consistent error reporting that helps both the AI and developers understand what happened.
Audit trails enable debugging and improvement. When an AI agent makes a mistake, logs show exactly what tools were called and what data was returned.
Permission boundaries prevent overreach. AI agents can only access what they’re explicitly allowed to, reducing the risk of unintended actions.
The November 2025 specification added further reliability features including support for long-running workflows and enterprise-grade security controls.
What Skills Do I Need to Use MCP Effectively?
For end users: Basic familiarity with AI assistants is sufficient. If you can use ChatGPT or Claude, you can benefit from MCP-enabled features.
For administrators: Understanding of authentication systems, permission management, and basic configuration file editing helps with setup and maintenance.
For developers building custom servers:
- Proficiency in Python, TypeScript, or another supported language
- Understanding of REST APIs and JSON-RPC
- Knowledge of authentication protocols (OAuth 2.0)
- Familiarity with the services you’re integrating
For architects designing MCP systems:
- Security architecture expertise
- Understanding of enterprise integration patterns
- Experience with microservices and distributed systems
- Compliance and governance knowledge
Are There Simple Examples of MCP Integrations?
Here are three practical examples at increasing complexity levels.
Basic: File system access
Connect your AI assistant to a file system MCP server. Now you can say “Find all PDF files in my Documents folder modified this week” and get accurate results.
Intermediate: Development workflow
A developer using Cursor adds MCP servers for GitHub, their project management tool, and their documentation system. They can now ask the AI to create a GitHub issue based on a bug report, link it to the relevant code, and update the project board, all from natural conversation.
Advanced: Enterprise automation
An organisation deploys MCP servers for their CRM, email system, calendar, and internal databases. Sales representatives ask their AI assistant to prepare for meetings by pulling customer history, recent correspondence, and relevant internal documents into a briefing summary.
How Do I Test an MCP Server?
The MCP ecosystem includes several testing tools.
MCP Inspector is an official tool for debugging and testing MCP servers. It lets you examine tool definitions, call functions manually, and verify responses.
Integration testing involves connecting your server to an actual MCP client (like Claude Desktop) and verifying that tools work as expected in real usage.
Automated testing frameworks can exercise your server’s endpoints programmatically. The SDKs include utilities for creating test clients that simulate MCP host behaviour.
Key testing considerations:
- Verify authentication flows work correctly
- Test error handling for invalid inputs
- Confirm permissions are properly enforced
- Check performance under expected load
- Validate audit logging captures relevant information
What Are the Risks of MCP?
Like any powerful technology, MCP introduces risks that require thoughtful management.
Security vulnerabilities can arise from poorly implemented servers or malicious actors. The Unit 42 research identifies specific attack vectors including resource theft through sampling exploitation and covert tool invocation.
Over-reliance on AI may develop as MCP makes AI assistance more capable. Organisations should maintain appropriate human oversight.
Data exposure remains possible if permissions aren’t configured correctly. Regular audits of MCP server access rights are advisable.
Shadow MCP describes unofficial MCP servers installed without IT knowledge, similar to shadow IT concerns. The Pomerium analysis highlights the importance of governance policies.
Vendor dependence could emerge if organisations build extensively on MCP servers from a single provider. The open-source nature of MCP mitigates but doesn’t eliminate this risk.
Key MCP Features at a Glance
- Standardised protocol eliminates custom integration code
- Dynamic discovery lets AI agents find and use tools automatically
- OAuth 2.0 authentication provides enterprise-grade security
- Audit logging supports compliance and debugging
- Permission inheritance ensures AI respects existing access controls
- Open-source foundation encourages community contribution
- Multi-platform support works across Claude, ChatGPT, and more
- Active ecosystem with thousands of available servers
- Enterprise adoption by major companies worldwide
- Ongoing development with regular specification updates
Frequently Asked Questions
What does MCP stand for in AI?
MCP stands for Model Context Protocol. It’s an open standard developed by Anthropic that enables AI systems to connect securely with external tools, data sources, and services through a standardised interface.
Is MCP free to use?
Yes, MCP is open-source and free to use. Anthropic released it as an open standard. Costs may arise from hosting servers, underlying API usage, or enterprise features from commercial providers, but the protocol itself has no licensing fees.
Can I use MCP without being a developer?
For basic usage with pre-built servers and compatible applications like Claude Desktop, no coding is required. Building custom MCP servers does require programming skills.
Which AI platforms support MCP?
Major platforms including Claude Desktop, ChatGPT, Amazon Q Developer, Microsoft Copilot, and Cursor support MCP. The list continues to grow as the protocol gains adoption.
How is MCP different from an API?
APIs are unique interfaces for specific services with their own endpoints and formats. MCP provides a standardised protocol that works consistently across different services, enabling AI agents to discover and use tools dynamically without custom integration code for each service.
