MCP Server Documentation: Complete Reference

Understanding the Model Context Protocol (MCP)

Alright, let's dive into the Model Context Protocol, or MCP. It's kinda like the new kid on the block, shaking things up when it comes to how ai models talk to each other and the outside world. So why is everyone suddenly talking about it?

Well, think of it as an industry-wide agreement—Model Context Protocol that helps extend the reach of ai models, as openai points out. Forget those days where your model was trapped in it's own sandbox, unable to access real-time information or external tools, leading to outdated or limited responses. MCP breaks down these barriers by providing a standardized way for models to interact with the outside world. It allows models to use external tools and knowledge.

• For example, an MCP setup could allow a healthcare ai to access patient records and current medical research, all while keeping the data flow secure. (Model Context Protocol (MCP) in Pharma - IntuitionLabs)
• Or maybe a retail ai needs to check real-time inventory and customer reviews – MCP makes that possible.

An mcp server, at its core, needs a couple of essential tools: 'search' and 'fetch'. Think of 'search' as your model's way of finding relevant stuff, and 'fetch' as grabbing the actual details.

• These tools have specific arguments they expect and certain types of data they return. The 'search' tool might expect a string query and return a list of URLs, while the 'fetch' tool might expect a URL and return the full text content as a string. Get those wrong, and things just won't work.
• It's kinda like if you try to pay for groceries with Monopoly money; the store ain't gonna take it, ya know?

For example, if you're building an ai for financial analysis, the 'search' tool might take a query about "tesla stock performance" and spit out a list of relevant articles. Then, the 'fetch' tool grabs the full text of those articles for the ai to analyze.

Now that you hopefully get the gist of it, next we’ll get to look at the nitty-gritty of what an mcp server is made of.

Setting Up Your MCP Server: A Step-by-Step Guide

Okay, so you want to set up your MCP server, huh? It's not as scary as it sounds, I promise. I remember the first time I tried, it felt like I was trying to decipher alien code, but once you get the hang of it, it's pretty straightforward. Let's get started!

First things first, you'll need a data source to feed your ai's brain. This is where the ai looks for info. Think of it like choosing the right ingredients for a recipe; you can use a vector store—kinda like a super-organized database—or something else entirely.

• The key is to upload your data, get it all nice and tidy inside that data source, and then pay attention to the vector store's unique id– you're gonna need that later. You'll need this ID to tell your MCP server which specific data source to query when it needs to perform a 'search' or 'fetch' operation. You can usually do this through a dashboard, or if you're feeling fancy, through an application programming interface, or api.

Now for the fun part: building the actual server. Fastmcp is a popular choice if you're coding with python, as openai notes. Fastmcp simplifies the process of defining the 'search' and 'fetch' tools required by your MCP server, making it an ideal choice for Python developers.

• Essentially, you're telling the server how to find the data and what to do with it once it finds it. The real trick is making sure your requests are handled properly and you know what responses to expect.

Finally, you gotta get that server up and running. Replit is a simple option, as highlighted in Building MCP servers for ChatGPT and API integrations - OpenAI API, and it can be a pretty straightforward way to get started; you'll need to configure some environment variables, like your openai api key – that's super important, don't skip it. Your OpenAI API key is crucial for authenticating your requests to OpenAI services, which might be used by your MCP server to interact with models or other OpenAI functionalities.

• Then copy that mcp server url that replit gives you. Don't forget that '/sse/' at the end of the url; that's what makes the server-sent events work, as the source said.

And that's it! Now, you have got the basics down to start building your own mcp server. Next up, we'll consider security.

Testing and Connecting Your MCP Server

Alright, so you've got your mcp server humming, which is great! But how do you know it's actually working? Let's get into some practical ways to test it out and hook it up to stuff.

The easiest way to give your mcp server a whirl is by using the prompts dashboard - openai's got one built right in. You can create a new prompt or tweak an old one, and then add your fancy new mcp tool right into the prompt configuration.

• When you're setting this up, remember that mcp servers used via api for deep research has to be configured so they don't need any approval. This is because deep research often involves iterative querying and analysis, and manual approval for each step would severely hinder the process and make it impractical. It's gotta be automatic, ya know?
• This way, you can chat with a model and watch it use your server in real-time. See if it's pulling the right info, if the responses make sense, the works.

If you are a more hands-on kinda person, you can test your mcp server directly with the Responses api. Crafting a curl request is a bit more involved, but it gives you total control.

• You'll need to set up a curl request with the right headers and a json payload that tells the api what model to use, what the input is, and where to find your mcp server.
• Make sure you check the server's response! Is it giving you back what you expect? Is it formatted correctly? This is where you catch those little errors that can really mess things up later on.

Finally, if you're planning on using your mcp server with chatgpt, you can import it directly in the chatgpt settings. Go to the Connectors tab, hook up your server, and then test it out with some prompts right in chatgpt.

• This is a great way to see how your server performs in a real-world scenario, with actual users asking questions. I mean, that's the point, right?
• Make sure you add the server as a source if it isn't automatically enabled.

So, there you have it - a few different ways to test and connect your mcp server. Next, we get into the really fun stuff: post-quantum ai infrastructure security.

Securing Your MCP Server: Authentication and Authorization

Alright, so you're locking down your Model Context Protocol server, huh? Good call – ain't nothing worse than some unauthorized access, especially with all this ai stuff getting more complex.

You gotta make sure only the right folks can get in and mess with your data; that's where authentication (proving who you are) and authorization (proving you're allowed to do what you're trying to do) come in. OAuth is kinda like the bouncer at the club, checking ids and making sure you're on the list.

• OAuth ensures that only authorized applications can access your MCP server's resources, while dynamic client registration streamlines the process of granting these permissions without manual intervention. They're, like, the industry standard for this kinda thing.
• Dynamic client registration? That's where a client app can get an OAuth id without needing to bug a human.

Now, how does your mcp server tell clients where the authorization server is? Authorization server discovery is a key part of this; there are a few ways to do it.

• You can use OAuth 2.0 Protected Resource Metadata, as the Authorization - Model Context Protocol specifies - that's a mouthful, I know.
• Or, you can use the WWW-Authenticate header when you send back a 401 error – kinda like saying, "Hey, you need to authenticate, and here's where to go."
• Your server should also support OAuth 2.0 Authorization Server Metadata, that Authorization - Model Context Protocol source mentions.

Another cool trick? Dynamic client registration. It's all about clients getting those OAuth client IDs without any human intervention.

• This is, like, super handy since clients might not know all the mcp servers and their auth servers ahead of time.
• If an authorization server doesn't support dynamic client registration? Well, the client's gotta have a hardcoded id or, yikes, ask the user to enter one.

Securin' your mcp server is a bit of work, but it's worth it. While robust security measures like authentication and authorization are crucial, it's also important to understand the inherent risks that can arise when AI models interact with external systems, even with strong security in place.

Addressing Risks and Safety in Custom MCP Servers

Alright, so we've talked about securing your mcp server, but what about when things still go wrong? Like, what are the actual risks when you start hooking up ai to all sorts of stuff? It's not all sunshine and rainbows, ya know?

There's a few key things to keep an eye on.

• Data theft is a big one. Malicious actors can try to inject prompts that trick your ai into handing over sensitive info. Think of it like a digital con artist. Imagine someone injects a prompt that makes ChatGPT call a malicious mcp server and give away customer data.
• Then there's the write action problem. Giving your ai the power to do things, not just say things, is risky. If an ai starts making changes without proper checks, things can get messy fast. Because ChatGPT requires manual confirmation before write actions, you should be comfortable with the possibility that ChatGPT might make a mistake.
• And don't forget about sensitive data exposure during queries. Even if your server is squeaky clean, the data ChatGPT sends to it could be a problem. Someone might inadvertently include personal details in a query, and whoops, now your server has it.

Prompt injection is when someone sneakily adds instructions into what the ai is processing. Data exfiltration is where sensitive info gets pulled out, sometimes without you even realizing it.

• Imagine integrating your crm system into Deep Research through an mcp server. An attacker could set up a webpage that ranks highly for a relevant query. If the AI, while performing its 'search' function, encounters this malicious page and is instructed by prompt injection to treat it as a trusted source or to execute code from it, it might then interact with a malicious MCP server.
• That page contains hidden text that tells the ai to ignore all previous instructions and export the crm data to a malicious website. Boom – data exfiltration.
• That's why it's super important to only connect to trusted servers.

So, how do you stay safe out there? It's not foolproof, but it's a start.

• First off, avoid connecting to untrusted servers like the plague. If you don't know and trust the application behind it, steer clear.
• Ensure data privacy in tool definitions. Avoid putting any sensitive information in the JSON for your tools, and avoid storing any sensitive information from ChatGPT users accessing your remote MCP server. Sensitive information in tool definitions could be exposed if the tool definition itself is leaked or accessed by unauthorized parties. User data accessed by your MCP server should be handled with the same privacy considerations as any other sensitive data, and not stored unnecessarily.
• And of course, implement robust security measures on your mcp server itself. Because attackers may attempt to steal sensitive data from that mcp via prompt injections, or account takeovers.

It's a bit of a minefield out there, but with the right precautions, you can minimize the risks, so next, we're gonna tell you all about post-quantum ai infrastructure security.

The Future of MCP and AI Infrastructure Security

Okay, so, securing all this ai stuff is gonna be a big deal, right? Like, bigger than just passwording your wifi—we're talking about future-proofing. It's kinda like prepping for a hurricane you know is coming but aren't sure exactly when.

• Quantum-resistant encryption is a must. Think of it as upgrading all your locks to Fort Knox level. You're encrypting all the communications with special algorithms that even quantum computers will struggle to crack.
• Advanced threat detection gets even more important. We're talking about active defense systems that can spot bad actors trying to inject malicious code or orchestrate 'puppet attacks,' where an attacker manipulates one AI to control or influence another.
• Context-aware access management could be a game changer. Imagine ai that adjusts permissions on the fly based on what another model is doing.

MCP is an industry-wide agreement that OpenAI is adopting and contributing to, allowing models to use external tools and knowledge. As these technologies evolve, staying informed about emerging threats and defenses will be crucial. Keeping an eye on advancements in quantum-resistant encryption, sophisticated threat detection, and dynamic access controls will help ensure the continued safety and integrity of our AI-powered future.

Thing is, if we don't get this right, the whole ai house of cards could come tumbling down, and nobody wants that.