The official Java SDK for building and consuming Model Context Protocol servers and clients, enabling Java apps to interact with AI models.
MCP Java SDK is the official Java implementation of the Model Context Protocol, providing tools for building and consuming MCP servers and clients. It enables Java applications to interact with AI models through a standardized JSON-RPC based interface, supporting both synchronous and asynchronous communication patterns. The SDK solves the problem of integrating AI capabilities into Java applications using a consistent, protocol-driven approach.
Java developers building AI-powered applications who need to integrate with Model Context Protocol servers or expose MCP endpoints. Spring AI developers looking for framework integration with MCP.
Developers choose MCP Java SDK because it provides the official, specification-compliant implementation for Java with pragmatic defaults and production-ready design. Its modular architecture, reactive programming model, and transport-agnostic approach offer flexibility while maintaining interoperability with the broader Java ecosystem.
The official Java SDK for Model Context Protocol servers and clients. Maintained in collaboration with Spring AI
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Implements the Model Context Protocol specification, enabling Java applications to communicate with AI models using a standardized JSON-RPC interface, as highlighted in the README's key features.
Uses Reactive Streams with Project Reactor for asynchronous and streaming interactions, with a synchronous facade for blocking use cases, providing scalability and approachability, as detailed in the architecture section.
Offers a pluggable JSON serialization abstraction with Jackson as the default, and transport-agnostic clients/servers, allowing integration with different stacks and reducing dependency bloat.
Integrates with SLF4J for logging and Reactor Context for propagating observability data, ensuring compatibility with modern monitoring tools without locking into specific implementations.
The conformance test results show client and auth suites are not fully passing (e.g., client has 9/10 checks passed), indicating potential gaps in specification compliance that may affect interoperability.
Running tests requires pre-installed Docker and npx, adding overhead and complexity to the development workflow, as mentioned in the 'Running Tests' section of the README.
The SDK provides only hooks for authorization without any implementation, forcing developers to rely on external libraries like Spring Security, which increases integration effort and potential for errors.