modelcontextprotocol

modelcontextprotocol/java-sdk

Java MIT AI & ML

The official Java SDK for Model Context Protocol servers and clients. Maintained in collaboration with Spring AI

3.6k stars
958 forks
active
GitHub +47 / week

3.6k

Stars

958

Forks

275

Open issues

30

Contributors

v2.0.0 11 Jun 2026

AI Analysis

The MCP Java SDK is the official Java integration for the Model Context Protocol, enabling Java applications to interact with AI models and tools through a standardized interface with both synchronous and asynchronous communication. It is purpose-built for Java developers and organizations adopting MCP within the Java ecosystem, particularly those using Spring AI; it is not a general-purpose AI library but rather a protocol implementation targeting enterprise Java applications.

AI & ML Library Discovery value: 3/10
Documentation 9/10
Activity 9/10
Community 8/10
Code quality 8/10

Inferred from signals mentioned in the README (tests, CI, type safety) — not a review of the actual code.

Overall score 8/10

AI's overall editorial judgment — not an average of the bars above, can weigh other factors too.

model-context-protocol java-sdk ai-integration spring-ai llm-integration
Actively maintained Well documented MIT licensed Niche/specialized use case Production ready
Deep Analysis · Based on README and public signals
5d ago

Official Java SDK for Model Context Protocol—18 months in, mature conformance, Spring AI integration ready

The Java SDK provides first-party support for MCP (Model Context Protocol), enabling Java and Spring applications to act as MCP clients and servers. Maintained by Anthropic in collaboration with Spring AI, it passes conformance tests at near-complete coverage, ships on Maven Central, and integrates directly with Spring Boot. Adoption spans enterprise Java shops building AI-aware microservices, though real-world production volume is not publicly documented.

Origin

Created January 2025 as part of MCP's multi-language SDK ecosystem. Roughly contemporaneous with C# SDK (4,367 stars) and ahead of Kotlin (1,400) and PHP (1,549) variants. Sits second in adoption after Python (23,528 stars), reflecting Java's enterprise weight but smaller share in early AI tooling.

Growth

Gained ~3,500 stars in ~18 months (modest 20 stars/week recently). Growth trajectory flat but consistent, typical of foundational infrastructure rather than end-user tools. Spring AI integration (announced in documentation) likely driving adoption among Spring developers, though explicit signal remains limited. No viral adoption moment; rather, steady positioning as the reference Java implementation.

In production

Adoption not verified. No case studies, deployment counts, or production user testimonials in README or repository metadata. Spring AI integration suggests interest from Spring ecosystem, but actual deployment scale unknown. Conformance test passing (high technical quality) does not equal production adoption.

Code analysis
Architecture

Based on README, SDK supports both synchronous and asynchronous communication patterns. Appears to provide separate client and server implementations. Spring AI extends the SDK with Boot starters, annotation-based method handling, OAuth 2.0 and API key security. README documents dependency BOM and Maven Central publication, suggesting modular, dependency-managed design. Actual transport layers, serialization strategy, and async runtime selection not detailed in truncated README.

Tests

Conformance test results published: Server 40/40 (100%), Client 9/10 checks (~90%), Auth 98.9% of checks. Validates against official MCP conformance suite at v0.1.15. Unit test execution requires Docker and Node.js; no line-coverage metrics stated in README.

Maintenance

Last push 2026-06-30, 5 days ago from evaluation date (2026-07-05). Build and snapshot publishing workflows active. Named maintainers (Christian Tzolov, Dariusz Jędrzejczyk, Daniel Garnier-Moiroux). Repository is young (created 2025-01-20) but actively maintained; no evidence of abandonment or long stale periods.

Honest verdict

ADOPT IF: You are building Java/Spring applications requiring MCP client or server capability and value conformance-tested, officially maintained code. Spring AI integration makes this the natural choice for Spring Boot shops. AVOID IF: You need production deployment patterns or battle-tested real-world examples—adoption evidence is sparse. You are building polyglot tooling and prefer language-agnostic transports (raw HTTP/gRPC may be simpler). MONITOR IF: You are waiting to see production adoption density, Spring AI MCP uptake acceleration, or emergence of enterprise case studies to reduce implementation risk.

Independent dimensions

Mainstream potential

5/10

Technical importance

7/10

Adoption evidence

2/10

Risks
  • Adoption not publicly verified: no disclosed production deployments, user counts, or enterprise case studies. Unclear if SDK is actually being used or mostly maintained for protocol completeness.
  • Spring AI coupling: While Spring integration is a strength, it may create perception that Java MCP is 'Spring-only,' limiting adoption among non-Spring Java shops (Quarkus, Micronaut, Vert.x).
  • Async model maturity unclear: README mentions 'asynchronous communication patterns' but truncation prevents assessment of reactive stack choice (Project Reactor, CompletableFuture, RxJava) and whether it fits streaming MCP workloads well.
  • Conformance gaps: Client passes 9/10 checks, Auth 98.9%—not 100%. Unclear if gaps are cosmetic or block real-world scenarios.
  • Early lifecycle: Created January 2025, only ~18 months old. MCP protocol itself is still evolving (conformance tests reference v0.1.15). Breaking changes or rapid API shifts remain possible.
Prediction

Java SDK will cement as the reference implementation for enterprise Java/Spring MCP adoption over next 12–24 months. Mainstream adoption likely limited to Spring AI ecosystem and early-adopter enterprises; unlikely to reach parity with Python in absolute volume. Technical quality and official backing suggest long-term viability, but real-world production density will determine whether it becomes standard or remains niche infrastructure.

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Languages

Java
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Information

Language
Java
License
MIT
Last updated
2d ago
Created
18mo ago
Analyzed with
anthropic/claude-haiku-4-5

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Contributors over time

Top 100 contributors only — repos with more will plateau at 100.

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vs. alternatives
Python SDK (23,528 stars)

Python dominates by 6.7x in stars; likely reflects Python's lead in LLM tooling and data science. Java SDK serves different ecosystem (enterprise backend) rather than competing for same users.

C# SDK (4,367 stars)

C# ahead by 1.2x. Reflects .NET's enterprise footprint. Java SDK is repositioned number-two among enterprise platforms, not a failure.

Spring Beans + manual HTTP (no dedicated SDK)

Enterprise Java teams could build MCP clients via REST/WebSocket libraries directly. This SDK removes boilerplate, conformance risk, and integration complexity.

Kotlin SDK (1,400 stars)

Kotlin is JVM-hosted but niche compared to Java. Java SDK likely subsumes Kotlin use cases for most teams.