http4k is a lightweight Kotlin HTTP toolkit that simplifies building MCP-compatible servers. It empowers developers to seamlessly integrate AI models with existing systems and data sources using familiar HTTP patterns. Leveraging http4k's Lens system, developers can create type-safe tool definitions and capability bindings with compile-time safety.

http4k supports various transport mechanisms, including HTTP streaming, SSE, WebSockets, and standard IO, facilitating integration with diverse AI applications. It fully supports MCP functionalities like prompts, tools, resources, completion, and sampling, accommodating both local and cloud-based models.

The core value lies in its testable design, enabling the creation of predictable and composable AI functions. Furthermore, http4k's serverless adapters allow deployment to platforms like AWS Lambda and GCP Functions without code modifications. With its pure Kotlin MCP protocol client, developers can build custom AI agents and integrate MCP functionalities directly into their applications.

Simple MCP Server Integration

http4k simplifies the creation of MCP-compatible servers by leveraging its familiar patterns. This allows developers to seamlessly integrate AI models with existing systems and data sources using a well-established and understood framework. Instead of wrestling with complex, AI-specific infrastructure code, developers can focus on defining the AI model's capabilities and how it interacts with the outside world. This streamlined approach significantly reduces the barrier to entry for incorporating AI into applications.

For example, a company with an existing e-commerce platform could use http4k to create an MCP server that connects their product catalog to a large language model. This would allow the AI to provide intelligent product recommendations or answer customer questions about specific items. The existing platform's data structures and APIs can be easily adapted to the MCP format using http4k's tools.

Type-Safe Tool Definitions

http4k's Lens system enables the creation of robust and type-safe AI integrations. This feature allows developers to define tools with strong typing, ensuring that the data passed between the AI model and external resources is validated at compile time. This reduces the risk of runtime errors and improves the overall reliability of the AI integration. By using Lens, developers can create clear contracts between the AI model and the external world, making it easier to reason about and maintain the system.

Imagine a scenario where an AI model needs to access a database to retrieve customer information. Using http4k's Lens system, the developer can define a tool that specifies the exact data types expected by the database query and the format of the returned data. This ensures that the AI model receives the data in the correct format and that any errors are caught early in the development process.

Flexible Transport Mechanisms

http4k supports a wide range of transport mechanisms, including stateful and stateless HTTP streaming protocols, Server-Sent Events (SSE), WebSockets, and standard input/output (I/O). This flexibility allows for seamless integration with various AI applications, whether they are desktop-based or web-based. The ability to choose the most appropriate transport mechanism for a given use case ensures optimal performance and compatibility.

For instance, a real-time AI-powered chatbot might benefit from using WebSockets for bidirectional communication, while a batch processing application could use standard I/O for efficient data transfer. The support for SSE enables the server to push updates to the client, which is useful for applications that require real-time information, such as stock market analysis or weather forecasting.

Testable and Pure Functions

http4k promotes the creation of highly testable MCP services by encouraging the use of pure functions and minimizing side effects. This allows developers to create predictable, testable, and composable AI functionalities that can be verified without running a server. The ability to easily test AI integrations is crucial for ensuring their reliability and correctness. By using pure functions, developers can isolate the logic of their AI services and test them in isolation.

Consider a function that processes user input and generates a response using an AI model. If this function is pure, its output will depend solely on its input, making it easy to test with different input values. This allows developers to verify that the function behaves as expected under various conditions, without having to worry about external dependencies or side effects.

Serverless Deployment Ready

http4k's serverless adapters enable the deployment of MCP functionalities to various FaaS (Function as a Service) platforms like AWS Lambda and GCP Functions without requiring any code changes. This simplifies the deployment process and allows developers to easily scale their AI-powered applications. The ability to deploy to serverless platforms reduces the operational overhead and allows developers to focus on building and improving their AI models.

For example, an image recognition service built with http4k can be deployed to AWS Lambda and triggered by uploading an image to an S3 bucket. The serverless adapter handles the integration with the Lambda environment, allowing the developer to focus on the image recognition logic. This makes it easy to create scalable and cost-effective AI services.