What is Model Context Protocol (MCP)?

Written by: Priyansh Khodiyar

The Model Context Protocol (MCP) is fast becoming a super important standard for the next wave of apps powered by Artificial Intelligence.

Large Language Models (LLMs) like GPT-4 or Anthropic’s Claude are remarkably smart within their own bubble of text. Yet historically, they’ve been trapped in silos – isolated from live data, tools, and actions in the real world. Integrating an AI assistant with external systems used to mean writing brittle, one-off code for each service (APIs, plugins, etc.), each with its own authentication, format, and quirks.

It’s like giving a genius robot a thousand different remote controls, each with a separate manual, and expecting it to use them all. Unsurprisingly, this approach doesn’t scale.

Model Context Protocol (MCP) is a new solution to this problem. Introduced by Anthropic (the company behind Claude) in late 2024, MCP is an open standard that provides a universal way to connect AI models to the places where data and tools live. Think of MCP as a kind of “USB-C for AI applications” – a single, standardized port through which an AI agent can plug into any compatible tool or data source.

In other words, MCP gives AI a consistent interface to interact with external resources, replacing the ad-hoc integrations of the past with a unified protocol built for AI agents. The goal is to move AI from being just a clever conversationalist to an actually useful agent that can fetch information, take actions, and maintain context across different systems.

Think of it as a major step up in how AI systems, especially those big brainy Large Language Models (LLMs), can actually connect to and use all the outside data and tools they need to do complicated stuff in the real world.

So, What Exactly is Model Context Protocol?

Before we talk more about MCP, here’s a quick announcement about our Hosted MCP server:

Basically, Model Context Protocol (MCP) is an open standard set up to totally change how Large Language Models (LLMs) and AI assistants link up with and use external data sources and tools.

Imagine MCP as the “USB-C port for AI apps.” You know how USB-C gives you one standard plug for all sorts of devices? Well, MCP does the same thing, but it’s a digital hookup for AI models to plug into different data banks, APIs (those things that let software talk to each other), and services.

The main reason for MCP is to give everyone a universal way to do things, so we don’t need a zillion custom-made, often clunky, connections anymore.

It’s all about making it smooth, safe, and efficient for AI systems to get hold of all sorts of information and tools, which makes them smarter, more capable, and more reliable. This isn’t just about making techies’ lives easier; it’s a smart move to make AI development more open and quicker for everyone.

By creating a common “language” for AI and tools to talk, MCP makes it easier for smaller companies and individuals to get in on the action. It helps build a richer, more varied AI world that isn’t just dominated by the tech giants. This means developers can build an MCP server for their tool or data just once, and instantly, any AI client or LLM that “speaks MCP” can use it.

**But Why Do We Need MCP?**

Hint – Solving AI’s Annoying Context and Connection Problems

MCP came about because it was really needed. It directly tackles some basic problems that have been holding back LLMs and other AI systems from being truly useful in day-to-day situations.

The LLM “I Don’t Know” Problem: LLMs are smart, but they’re often limited by the data they were trained on. If you ask them about something new, they might just say “I don’t know,” or worse, they might “hallucinate” and make stuff up. MCP helps fix this by giving LLMs a standard way to get the right information at the right time from live, external sources.
The Tangled Mess of Connections (The M×N Problem): Before MCP, if you had ‘M’ different AI models and ‘N’ different tools or data sources, you’d often have to build M×N unique, custom connections. This “integration spaghetti” is a nightmare – super complex, takes ages to build, costs a fortune to keep running, and just doesn’t scale up. MCP offers a “build once, connect to many” approach, which massively simplifies things. A tool provider can just set up their service with one MCP server, and then any AI model that understands MCP can use it.
Going Beyond Basic Fixes: Things like Retrieval Augmented Generation (RAG)—where LLMs grab info from outside knowledge bases to help answer questions—and basic function calling (letting LLMs trigger set functions) have been good first steps. But MCP wants to offer something more complete, solid, and universal. It doesn’t just standardize what (getting data or tools) but also how (the ways they talk, find each other, and work together).

How MCP Actually Works

MCP is designed to be clear and easy to expand, making it simpler for AI applications and the resources they need to work together in a standard way.

The Main Setup: The Client, Host, and Server Working Together

MCP works like a typical client-server setup, usually with three main players:

MCP Host: This is the app that wants to use AI and needs to get to outside data or tools using MCP. Think of AI chatbots like Anthropic’s Claude, coding environments like Cursor or VS Code with AI plugins, or other special AI tools. The host is like the conductor, managing the whole interaction.
MCP Client: This piece of software sits inside the MCP Host. It’s the part that actually talks to MCP Servers using the protocol’s rules. It does things like setting up connections, sending requests, and getting back answers.
MCP Server: An MCP Server is a simple program that acts as a doorway to a specific data source, API, or tool. It presents what that resource can do (like access a GitHub project, a Slack channel, a Google Drive, or a database) in a standard way that MCP Clients can understand.

Typically, it starts with the Host making a request (often because a user asked an LLM something).

The Client then talks to the right MCP Server, which gets the data or runs the tool needed.

The Server sends the result back to the Client, which then passes it to the Host. This often gives the LLM extra context to help it come up with a final answer.

Key Bits and Pieces: The “Language” of MCP

MCP Servers show what they can do through a set of standard “primitives,” which lets AI clients know what’s on offer:

Tools: These are like functions an AI model can run. This could be calling an API (like getting weather info), asking a database a question, reading or writing a file, or other specific actions. Tools come with descriptions (schemas) that say what information they need and what they give back, which helps the LLM understand them.
Resources: These are bits of data or content that LLMs can use for context. Resources are usually read-only and can be files, documents, database records, or any other info that helps an LLM give better, more relevant answers.
Prompts: MCP lets servers offer reusable, templated messages or pre-set workflows. These can help guide what users do or set up complicated tasks that need multiple steps or back-and-forths with the LLM and external tools.

A really important feature is Sampling. This lets the MCP Server ask the LLM (through the Client) to generate some text or do some thinking based on the information given. This allows for more complex, looping, and “agent-like” behaviors where the server can actively use the LLM’s brainpower while it’s doing its own job.

This setup, which mixes structured bits with the ability to discover things on the fly, is what makes MCP so powerful. It’s a step up from just simple, hard-coded API calls because it lets an AI client dynamically ask a server what tools and resources it has (for example, by asking for a tools/list).

This means the AI isn’t just making calls; it’s understanding what calls can be made and what data is available. That’s a much more robust and flexible way to build smart systems.

The Techy Stuff Behind It

Several common tech standards make MCP work:

JSON-RPC 2.0: This is a popular way to make remote procedure calls (basically, running a function on another computer). It’s used to format messages between clients and servers, making requests and responses structured and predictable.
Transports: MCP can work over different communication channels. For local stuff (like a server running on the same computer as the host), stdio (standard input/output) is common. For remote connections, HTTP with Server-Sent Events (SSE) is often used because it allows for streaming and real-time updates.
SDKs (Software Development Kits): To help people use MCP faster and make development easier, there are SDKs for popular programming languages like Python, TypeScript, Java, and C#. These SDKs give you ready-made libraries and tools to build MCP clients and servers.
Keeping Track & Agreeing on Features: Connections between MCP clients and servers are “stateful,” meaning they remember the context from one interaction to the next. When they first connect, the client and server do a bit of “capability negotiation” to figure out which features and protocol versions they both support, making sure they’re compatible.

Where Did MCP Come From? A Quick History

The Model Context Protocol was officially launched and made open-source by the AI research and safety company Anthropic in November 2024. Even though it’s pretty new, the ideas behind it and the problems it’s trying to solve have been around for a while.

A big inspiration for MCP was the Language Server Protocol (LSP). Microsoft brought out LSP in 2016 to standardize how code editors (like VS Code) talk to language analysis tools (language servers).

Before LSP, if you wanted to add good language support (like auto-completion, error-checking, and find-definition) for a new programming language to lots of different editors, you had to make N×M separate integrations.

LSP created a common protocol. This meant a single language server could work with many editors, and an editor could support many languages by just implementing the LSP client once. This massively improved language support in developer tools.

The folks who created MCP saw a similar situation happening with all the different AI and LLM tools popping up. With so many AI models, specialized tools, and data sources, it became clear that a similar unifying standard was needed.

LSP’s success was a strong example and a big reason MCP was designed the way it was. The hope is that MCP will have a similar huge impact on AI tools by making them work together better and simplifying how they connect.

At first, MCP’s announcement might have been a bit lost in all the excitement about how fast LLMs themselves were improving. But as developers and companies started to hit the real-world problems of building solid, connected AI applications, the value of a standard context protocol became much clearer. This led to it becoming more well-known in early 2025.

The fact that it’s an open standard is key to its vision. It encourages people to contribute, helps more people adopt it, and supports the growth of a rich collection of compatible tools and services.

Why Should You Even Care About Model Context Protocol (MCP)?

The arrival of MCP isn’t just another small tech update; it’s a fundamental change with big implications for anyone involved in making, using, or deploying AI. Understanding MCP is getting more and more important for staying ahead in a world where AI integration is everything.

A. For Developers

For software developers and AI engineers, MCP brings a lot of good stuff that can really streamline their work and open up new ways to be creative:

Simpler Connections & Less Hassle: The most obvious win is moving away from custom-made, often unreliable connections for every single AI model and tool. MCP gives you one, open protocol. This means developers can build an MCP server for their data source or tool just once, and then any MCP-friendly AI client can use it. This massively reduces that M×N connection headache.
Find Tools on the Fly & Make Them Work Together: MCP lets AI models find and use available tools and services automatically, without needing to be manually set up for every new thing. This helps create a “plug-and-play” environment where new features can be easily added to AI apps.
Focus on Cool Stuff, Not Plumbing: By taking care of the tricky low-level details of tool integration, MCP frees developers from writing the same boring code over and over. This lets them focus on the bigger picture: application logic, user experience, and creating new and exciting AI-driven features.
Better Developer Life: Having SDKs in different languages, a growing collection of ready-made MCP connectors for popular services (like GitHub, Slack, databases), and the ability to quickly try out ideas and set up workflows make the whole development process much nicer.

The real game-changing potential of MCP for developers might go beyond just connecting AI to existing tools. It could lead to the creation of totally new, AI-native tools and services – things designed from day one to be used via MCP.

A universal standard like MCP makes it more worthwhile for developers to build tools specifically for AI, knowing there’s a big, compatible audience of clients out there.

These AI-native tools could offer finer control, richer information, or features uniquely suited for LLM interaction that general-purpose APIs might not have. This could even create a new marketplace for MCP-enabled services, kind of like API marketplaces but specifically for AI agents.

B. For Businesses & Big Companies

For organizations wanting to use AI’s power, MCP offers some pretty attractive strategic benefits:

Faster AI Projects & Quicker Results: By standardizing connections and letting businesses use pre-built integrations, MCP helps them roll out AI solutions more quickly and efficiently. This means they see a return on their AI investments sooner.
Smarter AI & More Capable Apps: MCP gives AI agents secure, real-time access to important company data and functional tools. This leads to AI applications that are more accurate, understand context better, are more relevant, and ultimately, much better at solving business problems.
More Efficiency & Productivity: The ability to streamline complex workflows and automate tasks across many different systems using AI agents that talk through MCP can lead to big improvements in how efficiently things run and how productive employees are.
Scalability & Being Ready for the Future: MCP’s often cloud-friendly design and its standardized approach mean that AI solutions built with it can more easily grow as business needs increase. It also helps “future-proof” things, because new AI models or tools that follow the MCP standard can be added with less fuss.
Less Vendor Lock-in: Because MCP is open and doesn’t care which AI model you use, it gives businesses more freedom. They’re less likely to be stuck with one specific LLM provider or a proprietary set of tools. This lets them pick the best components for their AI solutions.
Better Security and Control: MCP is being built with security as a top priority. Features that support encryption, detailed access controls, ways for users to approve sensitive actions, and the option to host MCP servers themselves give companies more control over their data and AI interactions. Application owners can still decide exactly which application functions AI agents can access, which helps with compliance and reduces risks. This focus on security and governance shows the AI industry is maturing and moving towards production-ready, enterprise-grade solutions. This is vital for building trust and getting MCP adopted in sensitive areas like finance and healthcare.

C. Real-World Impact

MCP is already starting to be used in various areas:

Software Development: AI-powered coding assistants are using MCP to get real-time access to code context right inside IDEs. This helps with “vibe coding” (where developers just describe what they want in plain English), automates creating pull requests, and lets them query databases without switching windows.
Enterprise AI Assistants: Businesses are using internal AI assistants that use MCP to connect to their own document systems, Customer Relationship Management (CRM) systems, internal knowledge bases, and other company apps. This helps employees find information quickly and automate everyday tasks.
Digital Media & Personalized Content: MCP can power super-personalized content recommendations by letting AI understand what users like in great detail. It can also enable adaptive video streaming (changing content based on how engaged someone is) and help with smart content tagging and automated video editing.
Financial Markets: Financial institutions are looking at MCP for AI-driven automation in areas like processing trades, managing risk, and connecting with complex trading applications. Genesis Global, for example, launched an MCP server to control how AI agents interact with software built on their platform, allowing for complex business outcomes by combining operations from Genesis and other MCP-enabled applications.
Data Analysis & Business Intelligence: MCP makes it easier to use natural language to access SQL databases, so users can get data without writing complicated code. It’s also being used to connect AI to tools that analyze logs and event data, like the Axiom platform.
Productivity & Automation: Everyday productivity can get a boost from AI agents using MCP to manage calendars, use communication platforms like Slack, access files on Google Drive, or process payments via Stripe.

D. The Ecosystem is Growing

A really good sign of MCP’s potential is how quickly key players in the AI and tech world are adopting it. Anthropic, who started it, naturally champions the protocol.

Crucially, other major AI developers like OpenAI (jumped on board in March 2025) and Google DeepMind (announced support in April 2025) have also embraced MCP. This signals a move towards a standard that everyone in the industry can use.

Microsoft has been actively involved, building MCP into its Azure AI services and partnering with Anthropic on the C# SDK. GitHub has released its own open-source local MCP Server, allowing integration with GitHub APIs.

Companies like Replit, Sourcegraph, Zapier, Workato, and financial tech provider Genesis Global are also adding MCP to their platforms and products.

The Spring AI framework now supports dynamic tool updates via MCP, meaning AI capabilities can be extended on the fly.

This rapid adoption by influential companies is a direct result of the “M×N integration problem” becoming a real headache as these companies try to scale up their LLM offerings and build strong ecosystems around them.

MCP offers a practical way to deal with this growth challenge and encourage more third-party tool development, making it a strategic must-have for creating the rich ecosystems their platforms need to succeed.

Beyond big companies, a lively community is growing, with an increasing number of open-source MCP connectors and server setups becoming available. This further speeds up the protocol’s spread and usefulness. This momentum is crucial for making MCP a lasting standard.

To show the different benefits more clearly, here’s a table summarizing what MCP offers to different groups:

Table: Benefits of Model Context Protocol for Different Stakeholders

Stakeholder	Key Benefits with MCP
Developers	Less integration hassle, faster prototyping, find tools on the fly, focus on new ideas, better developer experience.
Enterprises	Faster AI rollout, better AI capabilities, improved operational efficiency, cost savings, easier scaling, strong security.
End-Users	More context-aware and reliable AI assistants, personalized experiences, access to real-time info.
Tool Providers	Easier integration with lots of AI clients, wider reach for their services and APIs.

The Future of MCP: Trends, Challenges, and What’s Next

As Model Context Protocol moves from being a new idea to an increasingly used standard, its future will be shaped by new tech trends, how well it handles its challenges, and what the AI community as a whole does.

A. Emerging Trends: How AI Interaction is Evolving

MCP is set to be a driving force for several exciting trends in AI:

Multi-Agent Systems: MCP provides a natural way for more advanced multi-agent AI systems to communicate. In these systems, different specialized AI agents could work together on complex tasks, each using MCP to get the specific tools and data they need. The example of loan underwriting, with different AI “personas” for loan officer, credit analyst, and risk manager, shows this potential.
Hyper-Intelligent Content Systems: MCP’s ability to provide real-time, detailed context could let AI systems dynamically create, adapt, and personalize content with a level of relevance to individual users and situations we’ve never seen before.
AI-Native Platforms: We’re likely to see the rise of platforms and services built with MCP at their core. These “AI-native” platforms will offer AI services that are easy to combine, adaptable, and inherently secure, designed from the start for smart automation.
Making Tool Building and Discovery Easier for Everyone: As more people adopt MCP, more developers and organizations will be ableto create and share MCP servers for a huge range of tools and data sources. This will lead to a rich ecosystem, making AI capabilities more accessible to everyone. The emergence of MCP marketplaces or registries will be vital for finding and managing these servers, much like how npm helps the JavaScript community or app stores help mobile ecosystems. This is a natural next step: as the number of MCP servers explodes, finding them becomes the new challenge, which directly calls for centralized places to list them.
Software Becoming More “Agent-Like”: A major implication of MCP’s success could be that software becomes more “agentified.” Users might interact less with traditional GUIs (graphical user interfaces) and more with AI agents that use MCP to manage tasks across various backend systems. This could fundamentally change how software is designed and used, with a greater focus on conversational interfaces and AI-driven workflows.

Getting Over the Hurdles

Despite its promise, MCP faces several challenges that need to be tackled for it to become widely and sustainably successful:

Security and Trust: This is probably the biggest challenge. Since MCP gives AI models access to potentially sensitive data and powerful tools, security risks like data leaks, unauthorized tool use, content poisoning (bad data messing with AI behavior), prompt injection attacks, and authentication weaknesses must be handled very carefully. Strong security practices, thorough auditing, detailed access controls, and clear user consent are essential. Ongoing research and community watchfulness will be key.
Standardization vs. Breaking Apart: While MCP aims to be a universal standard, there’s a risk that competing protocols or different versions of MCP could pop up. This could lead to fragmentation and weaken its main benefit of making everything work together. The tension between having one universal standard and meeting the diverse, complex needs of specific fields (e.g., finance needing stricter security than a public data tool) might mean we need “MCP Profiles” or specialized extensions. These would allow a core standard to ensure basic interoperability while also fitting domain-specific needs for security, compliance, or data handling. This would prevent the main protocol from becoming too rigid or too loose.
Scalability and Performance: As more MCP systems are deployed, making sure the protocol can efficiently handle many clients and servers at once, especially for real-time, stateful interactions, will be critical. Performance bottlenecks could slow down adoption for demanding applications.
How Mature the Tools and Documentation Are: While it’s improving fast, the collection of SDKs, developer tools, and detailed documentation for MCP might still have gaps or inconsistencies across different programming languages and platforms. The quality and maintenance of community-contributed connectors can also vary, which can affect reliability.
Complexity for Certain Uses: MCP as it stands might not be perfect for all types of tool interactions, especially those that need highly custom or deeply stateful patterns that the protocol doesn’t yet handle elegantly, or for use cases that need extremely tight, super-fast connections.
Managing Identity and Authentication: Currently, how authentication and identity are managed is often left up to individual server setups or how they’re deployed. As the ecosystem grows, a more standardized or integrated approach to these crucial aspects might be needed to ensure consistent security and trust.

Keeping Up: Recent Developments in MCP (Late 2024 – Mid 2025)

The period from late 2024 through mid-2025 has been a really important time for Model Context Protocol, with big announcements and growing momentum:

Launch and Early Buzz (Late 2024 – Early 2025): Anthropic officially launched MCP in November 2024. While it was initially overshadowed by LLM releases, by early 2025, people started realizing its strategic importance as teams struggled with connecting AI agents to real-world data.
Major Adoptions (Q1-Q2 2025):
- OpenAI: Announced official adoption of MCP in March 2025, with plans to integrate it into its products, including the Agents SDK and ChatGPT desktop app.
- Google DeepMind: CEO Demis Hassabis confirmed MCP support within the Gemini SDK in April 2025, calling it a “rapidly emerging open standard.”
- Microsoft: Built MCP into Azure AI services and co-released the C# SDK with Anthropic. The Microsoft Learn Blog also provides ongoing updates relevant to AI skills, which indirectly supports the ecosystem for protocols like MCP.
New SDKs and Server Setups:
- The C# SDK was released, adding another language option for MCP development.
- Spring AI introduced dynamic tool updates for its MCP implementation in May 2025. This allows MCP servers to add or remove tools on the fly without restarting, and clients can detect these changes immediately.
- Genesis Global launched an MCP Server in May 2025, specifically designed to enable AI-driven automation and innovation in financial markets by letting AI agents interface with applications built on the Genesis platform.
Growing Ecosystem and Chatter:
- GitHub highlighted MCP’s evolution towards multi-agent systems in an April 30, 2025 blog post, pointing to its foundational role in new open-source AI projects.
- Anthropic announced on May 1, 2025, that its AI model, Claude, can now connect to a user’s world through integrations, likely using MCP.
- Numerous tech publications and blogs began deep-dive analyses of MCP, its benefits, and challenges throughout early to mid-2025.
- An arXiv paper published in March 2025 (updated April 2025) gave a thorough academic overview of the MCP landscape, security threats, and future research directions.
Security Discussions: Alongside rapid adoption, security researchers started publishing analyses in April-May 2025, highlighting potential weaknesses in MCP implementations and the protocol itself. This emphasized the need for ongoing security attention.

These developments show a period of intense activity and validation for Model Context Protocol, cementing its position as a key standard to watch in the AI industry.

Conclusion: Why Understanding MCP (Especially Model Context Protocol) Matters More Than Ever

Model Context Protocol is quickly becoming not just a tech standard, but a key piece that makes the next generation of smart, autonomous digital systems possible. Its role in standardizing how AI models connect to and interact with the world’s massive amounts of data and functional tools is a real game-changer.

For developers, it promises to simplify things and open up new ways to innovate. For businesses, it offers a path to more powerful, efficient, and scalable AI solutions, helping them get results faster and potentially reducing their reliance on single vendors.

The big idea that subtly links the various “MCPs” we’ve talked about is a fundamental push towards standardization, validation, and structured interaction within increasingly complex systems.

In an era of fast-changing technology, especially one so heavily influenced by AI advancements, understanding foundational protocols like the Model Context Protocol is no longer optional for those in the field—it’s essential.

Staying informed about its developments, who’s adopting it, and the ecosystem growing around it will be key to navigating and shaping the future of AI and how it’s integrated into every part of our digital lives.

MCP’s ability to bridge the gap between the abstract intelligence of LLMs and the concrete realities of real-world data and actions is what makes it, and by extension, understanding such pivotal “MCPs,” matter more than ever.

Frequently Asked Questions (FAQ) about Model Context Protocol

This section tackles common questions about Model Context Protocol to give quick, clear answers, helping you understand it better and covering specific things people search for.

What’s the main difference between Model Context Protocol (MCP) and a regular API?

A regular API (Application Programming Interface) is like a rulebook for how software programs can talk to each other and share information or functions. An API offers specific “endpoints” that provide certain data or do certain things.

Model Context Protocol (MCP), however, is a broader open standard specifically for how Large Language Models (LLMs) and AI agents find, connect to, and use these APIs (and other data sources and tools) in a consistent way.

MCP lays out how an AI client can learn what an MCP server can do (its tools, resources, prompts) and how to use them. Basically, MCP provides a standard “language” or “adapter” for LLMs to use various APIs and tools, while the APIs themselves are the actual services being used.

Is Model Context Protocol (MCP) open source?

Yes, Model Context Protocol is an open standard that Anthropic made open-source. This encourages the community to contribute, helps more people adopt it, and keeps things transparent.

Who created the Model Context Protocol (MCP)?

Anthropic, an AI safety and research company, introduced and open-sourced the Model Context Protocol in November 2024.

Which companies are adopting Model Context Protocol (MCP)?

A growing number of major tech companies and AI developers are adopting MCP or have announced they’ll support it. This includes its creator Anthropic, as well as OpenAI, Google DeepMind, Microsoft, GitHub, Replit, Sourcegraph, Workato, and Spring AI, to name a few.

How does MCP help reduce LLM hallucinations?

LLM hallucinations (when models generate incorrect or nonsensical information) often happen when the model doesn’t have enough good, accurate information, or when it misunderstands how to use an external tool. MCP helps with this by:

Giving LLMs access to real-time, specific, and relevant information from external data sources through standardized “Resources.”
Offering a structured way for LLMs to understand what external “Tools” can do, what information they need, and what they give back. This makes interactions more predictable and less likely to go wrong compared to less clearly defined ways of connecting.

By basing LLM responses on verified external information and clearly defined tool interactions, MCP reduces how much the LLM has to rely on its (possibly outdated or incomplete) training data.

Can I build my own MCP Server?

Yes, developers can build their own custom MCP servers. Software Development Kits (SDKs) are available in various programming languages, including Python, TypeScript, Java, and C#. These make it easier to create servers that offer specific tools, data sources, or functions according to the MCP standard.

Is MCP secure?

MCP is designed with security in mind. For example, it encourages user consent for actions, allows for access controls, and supports secure communication channels.

However, how secure an MCP setup ultimately is depends a lot on how the MCP servers and clients are actually built and on the security practices of the developers. There’s ongoing research and discussion in the community about potential security risks (like prompt injection, tool permission issues, and authentication weaknesses) and the best ways to deal with them.

Organizations need to do thorough security checks and put strong measures in place when using MCP-based solutions, especially if they involve sensitive data or critical operations.