Deploying MCP Servers on AWS Lambda

Understanding MCP and Serverless Architecture

Okay, so you're probably wondering what all the hype around MCP is about, right? It's not just another acronym floating around the ai space, it's kinda a big deal.

• Model Context Protocol (MCP) acts like a translator, think of it as the universal language that allows Large Language Models (llms) to talk to different tools, like databases or apis. Instead of every ai needing custom code for each tool, MCP gives 'em a standard way to connect.

• A standardized protocol for tool interaction is good for everyone. (What is Model Context Protocol (MCP)? - IBM) It simplifies integrations, kinda like how usb ports made connecting devices way easier. This means less headaches and more focus on building cool stuff instead of wrestling with compatibility issues.

• Traditional ways of doing this, well, they can get messy. (Tips for a messy but clean person : r/CleaningTips - Reddit) Imagine trying to build a custom connector for every single tool you want your ai to use, it's just not sustainable. MCP solves that.

Need an example? Think about a retail ai using MCP to check current inventory levels, or a healthcare ai scheduling appointments by talking to the hospital's system. The finance sector could also use these tools for fraud detection.

Next up, we'll explore why aws lambda is such a good fit for running MCP servers. These benefits make Lambda an ideal platform for our MCP server, and here's how we'll leverage them by building a stateless server that scales automatically and is cost-efficient.

Building a Stateless MCP Server on AWS Lambda

Okay, so you wanna run an MCP server on aws lambda? Cool, let's dive in! It's not as scary as it sounds, I promise.

Okay, so what's the big deal about running an mcp server on lambda? Well, for starters, it means you can scale instantly, without, you know, actually managing servers. Serverless is all about event-driven execution, and Lambda fits that bill perfectly. Plus, it's way more efficient.

• Instant Scalability: AWS Lambda auto-scales, so it can handle sudden spikes in request without breaking a sweat. No need to provision or manage any servers!
• Cost Efficiency: You only pay for what you use. Lambda is great for saving money, especially for services with intermittent traffic.
• Event-Driven Execution: Lambda functions are triggered by events, making it ideal for handling mcp requests as they come in.

So, how do we actually build a stateless mcp server on lambda? Well, it involves defining your tools, dockerizing them, and then deploying 'em. featureform has a good writeup on this.

1. Tool Definition: You gotta define your tools using something like mcpengine. This involves registering the function with the mcp manifest. An MCP manifest is essentially a configuration file that describes the available tools, their parameters, and how they should be invoked by the MCP protocol.
2. Dockerizing: Next, containerize your server. This typically involves creating a Dockerfile that specifies the base image, dependencies, and how to run your application. You'll then build this into a Docker image.
3. Deployment: Push the image to Amazon ecr and configure your lambda function. This usually means creating a new Lambda function, selecting the container image as its deployment package, and setting up triggers.

Once deployed, you connect to it using a compatible llm client, like Claude. This involves setting up the authorization flow and tool selection process. As FeatureForm notes, you then can connect your function to Claude.

Now that we has a handle on building the server, let's dive into defining a simple mcp tool. However, it's important to remember that statelessness, while efficient, might not be suitable for all applications that require persistent data or session management. For those scenarios, we'll need to consider stateful solutions.

Implementing a Stateful MCP Server with RDS

So, you've built your stateless MCP server. Now, let's get real – most apps need to hold onto data, right? Think of it like this: a chatbot that forgets everything after each message isn't very useful. That's where rds comes in.

We're gonna use Amazon rds to store data. It's like giving your mcp server a brain with memory.

• Persistent Storage: RDS lets us store data persistently. Whether its user profiles in a social media app, product catalogs for an e-commerce store, or patient records in healthcare app, you name it.
• Database Schema: Imagine a chat app. You'd need tables for users, messages, and maybe chatrooms. Each table has columns, like message id, user id, timestamp, and the actual text.
• SQL Schema Example: Here's a simple messages table:

  CREATE TABLE messages (
    id SERIAL PRIMARY KEY,
    username TEXT NOT NULL,
    text TEXT NOT NULL,
    timestamp TIMESTAMP DEFAULT now()
  );
  

You don't want to open a new database connection every time a tool function runs. That's super inefficient. Instead, we use a context handler. A context handler is a mechanism that encapsulates request-specific data and resources, including a database connection pool, to ensure efficient and isolated processing of each request. This'll create a connection pool when the server starts up and attach it to each request. FeatureForm uses a context handler to manage a connection pool.

Now, lets implement some tools that actually use the database. For example, a tool to post messages and another to get recent messages. Remember to handle your database interactions carefully inside the lambda functions to avoid bottlenecks.

Next up, we'll deploy and test this stateful server.

Securing MCP Servers with Authentication

Okay, so you've got your MCP server humming along, but is it really secure? Letting just anyone access your tools? Nah, that's a recipe for disaster, right?

• OAuth to the Rescue: We're gonna use Google OAuth to lock things down. Think of it like a bouncer for your MCP server.

  • First, you'll register a Google OAuth app, which gives you a Client ID and Secret. These are like the username and password for your server to talk to Google.
  • Make sure you set up an authorized redirect URI; for testing, http://localhost works just fine. This tells Google where to send users after they log in.

• MCPEngine Authentication: Now, you need to tell your MCP server to use these credentials.

  • You'll configure MCPEngine to use token-based authentication with OIDC (OpenID Connect). OIDC is an authentication layer on top of the OAuth 2.0 protocol, allowing clients to verify the identity of the end-user based on the authentication performed by an authorization server, and obtain basic profile information about the end-user.
  • Use @engine.auth() to protect your tools. This means only users with a valid token can access them.

• Client-Side Updates: Your client, like Claude, needs to play ball too.

  • You'll update the client to pass a valid, Google-issued ID token.
  • This usually involves requesting a token from Google after the user logs in.

from mcpengine import MCPEngine, GoogleIdpConfig
from mcpengine.context import Context # Assuming Context is available here

"""
 app_lifespan is a variable that defines how long the application instance should live.
 For AWS Lambda, this might relate to the execution environment's lifecycle.
 A common approach is to set it to a reasonable duration or manage it based on
 Lambda's timeout settings. For this example, let's assume it's defined elsewhere or
 can be a placeholder. If not essential for the core example, it could be removed.
 For demonstration, let's define it simply:
"""
app_lifespan = 3600 # Example: 1 hour in seconds
engine = MCPEngine(
    lifespan=app_lifespan,
    idp_config=GoogleIdpConfig(),
)
@engine.auth()
@engine.tool()
def post_message(text: str, ctx: Context) -> str:
    """Post a message to the global timeline."""
    # Only runs if the token is valid
    # ... database interaction would happen here using ctx.db_pool
    return "Message posted!"

After redeploying, Claude should now prompt users to authenticate via Google before they can use the protected tools. The call will only work if a valid token is present. FeatureForm's write-up shows how to configure MCPEngine with Google's public JWKS endpoint. JWKS (JSON Web Key Set) is a set of keys used to verify the signature of JSON Web Tokens (JWTs). Google's public JWKS endpoint provides the public keys necessary to validate tokens issued by Google.

Next, let's talk about how to handle access scopes – cause not everyone should have access to everything, right?

Access Scopes for Granular Control

Okay, so you've got your MCP server secured with authentication, but what if you want to give different users access to different tools or even different functionalities within a tool? That's where access scopes come in.

• What are Access Scopes? Think of scopes as permissions. Instead of just saying "yes, you're logged in," scopes specify what you're allowed to do. For example, one user might only be allowed to read data, while another can read and write.

• Why are they Important? Implementing scopes is crucial for the principle of least privilege. It ensures that users and other services only have the minimum necessary permissions to perform their tasks, significantly reducing the attack surface and the potential impact of a compromised account.

• How to Implement Them? You can implement access scopes by extending your authentication flow. When a user logs in, you can request specific scopes. These scopes are then included in the ID token. Your MCP server can then check these scopes before allowing a tool to execute. For instance, you might have a scope like read:inventory or write:appointments. You'd then modify your tool decorators or logic to check if the authenticated user's token contains the required scopes.

With access scopes, you can build more sophisticated and secure MCP applications that cater to diverse user roles and requirements.

Advanced Considerations and Best Practices

So, you've built your MCP server, that's great! But just deploying it isn't enough, you know? You gotta think about keeping it secure, running smoothly, and scaling when things get busy.

Security's gotta be job one.

• Rate limiting is super important to stop those pesky DDoS attacks. Think of it like this: if someone's hammering your door, eventually, you just stop answering, right?
• And don't forget about AWS WAF – it's like having a bodyguard against common web exploits. It'll filter out the bad stuff before it even hits your server.
• Always follow the principle of least privilege for IAM roles. Give your functions only the permissions they absolutely need, nothing more. It's like only giving someone the keys to one room instead of the whole building.

You can't fix what you can't see, right?

• Centralized logging using CloudWatch is key. It's like having a security camera system for your server.

• Keep an eye on your server's behavior, so you can troubleshoot issues.

• Configure those retention periods in CloudWatch to save on storage costs.

• Optimize your Lambda function configuration for top-notch performance. This can include adjusting memory allocation (more memory often means more CPU power), choosing the right runtime, and optimizing your code for faster execution.

• Use CloudFront to cache content and boost global performance.

• Implement auto-scaling policies to adjust capacity based on demand. For Lambda, this is largely inherent. Lambda automatically scales based on the number of incoming requests. You can manage concurrency limits to control how many requests a function can handle simultaneously, which indirectly influences its scaling behavior.

With these practices in place, your MCP server should be well-protected, efficient, and ready for whatever comes its way. Now, let's talk about cost optimization and future directions.

Conclusion

Alright, so we've reached the end of our journey, huh? Hopefully, you're not too overwhelmed by all this mcp on lambda stuff. It can seem like a lot at first.

Let's do a super quick recap, shall we? We talked about:

• Building a stateless MCP server using aws lambda, which let's you scale without having to manage servers yourself. It's kinda like magic, but with code.
• Implementing a stateful MCP server with rds, so your ai can actually remember stuff from one interaction to the next.
• Securing your server with Google oauth, because you don't want just anyone messing with your stuff.
• And we touched on access scopes for finer-grained control.

These steps, when brought together, creates a serverless architecture that's not only secure but also super scalable. Imagine a healthcare app, using this setup to securely access patient records, or a finance ai detecting fraud in real-time.

The future of mcp and agentic systems is looking pretty bright. As the featureform post hints, we might see ai agents collaborating more seamlessly, exchanging ideas and automating complex tasks. This could manifest in more sophisticated multi-agent systems where agents can dynamically discover and utilize new tools, or even in agents that can autonomously reason about complex problems and break them down into smaller, manageable tasks. We might also see novel integration patterns where agents can orchestrate workflows across disparate services with minimal human intervention.

So, if you're thinking about diving into ai and llms, understanding mcp and how to deploy it on aws lambda is a skill worth having. Who knows, maybe you'll be the one building the next big thing in ai!