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modelcontextprotocol/java-sdk

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

Hosted by Model Context Protocol · part of Agentic AI Foundation

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modelcontextprotocol/java-sdk has 3.5k+ stars and 1k+ forks on GitHub, is written in Java, and was last pushed on Oct 16, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 2 hours ago
Type:Library / SDKCategory(s):MCP Servers, Agent Skills & PromptsAgent Frameworks & SDKsAI Agents & LLM Apps
Added to GitGenius on September 18th, 2026
Created on January 20th, 2025
Open Issues & Pull Requests: 282 (+0)
GitHub issues: Enabled
Number of forks: 1,176
Total Stargazers: 3,741 (+0)
Total Subscribers: 84 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 5.9 days
Mean response time: 55.1 days
90th percentile: 223.5 days
Tracked items: 450

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Maintainer activity

8 people did triage or write work on this repository in the last 12 months.

Counts unlabeled, assigned, unassigned, milestoned, demilestoned, locked, unlocked over the last 12 months. These are issue and pull request events that require triage or write permission. Commits and code review are not counted. labeled and renamed are excluded because GitHub issue forms record the issue author as the actor. Figures from October 11, 2026. This count is not comparable across projects: each project's automation decides which of these events a person emits. How this is counted.

How this project is maintained

About 11% of issues opened in the past year have never received a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. 50% of tracked open issues have had no activity in three months. Only 59% of issues opened in the past year have been closed. Three people close 85% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 113
New in 7 days: 7
Closed in 7 days: 4
Avg open age: 233 days
Stale 30+ days: 81
Stale 90+ days: 52

Recent activity

Opened in 7 days: 7
Closed in 7 days: 4
Comments in 7 days: 3
Events in 7 days: 25

Top labels

  • enhancement (128)
  • bug (71)
  • P2 (60)
  • waiting for user (50)
  • P3 (48)
  • P1 (41)
  • wontfix (33)
  • area/server (32)

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Detailed Description

The MCP Java SDK is an official Java SDK for implementing Model Context Protocol servers and clients. It enables Java applications to interact with AI models and tools through a standardized interface, supporting both synchronous and asynchronous communication patterns. The SDK provides core abstractions for building MCP-compliant servers and clients that can communicate with each other following the Model Context Protocol specification.

The tool solves the problem of integrating Java applications into the Model Context Protocol ecosystem. It abstracts away the complexity of implementing the protocol by providing ready-made components for both client and server roles. Developers can use the SDK to either build MCP servers that expose tools and resources to AI models, or build MCP clients that consume services from other MCP servers, without needing to implement the protocol details themselves.

Java developers building AI-integrated applications should choose this SDK if they need native Java support for the Model Context Protocol. It suits projects that require both client and server capabilities within the same codebase, or teams already invested in the Java ecosystem. Spring Boot developers have an additional advantage through Spring AI integration, which provides boot starters and annotation-based method handling that reduce boilerplate configuration. The tool requires Java 17 or later.

The project maintains active conformance validation against the official MCP test suite, with server implementations passing all tests and client implementations passing the majority of checks. Development includes comprehensive documentation covering architecture, client usage, and server implementation. The codebase is validated through automated testing that requires Docker and Node.js tooling, indicating a mature testing infrastructure. Collaboration with Spring AI brings additional ecosystem integration and security features including OAuth 2.0 and API key support.