Best MCP Servers for Java Spring Boot Development in 2026

Java Spring Boot development has a unique relationship with AI coding assistants. On one hand, the framework is mature, well-documented, and heavily convention-driven -- exactly the kind of ecosystem where an AI assistant should excel. On the other hand, Spring's annotation-heavy API surface changes significantly between major versions, JPA entity relationships have subtle behaviors that training data often gets wrong, and the verbose stack traces that Java produces can overwhelm a context window if you are not careful about what you feed the model.

MCP servers address these friction points by giving your AI assistant live access to the systems that matter: your database schema, your Docker containers, your CI pipeline, your error tracker, and current documentation. Instead of pasting stack traces into a chat window or manually describing your entity relationships, the assistant can query your database directly, check your container logs, and pull up the correct Spring Boot 3.x documentation.

This guide covers the five best MCP servers for Java Spring Boot developers and how they fit into the typical Spring workflow.

PostgreSQL MCP -- JPA Without the Guesswork

Author: Anthropic (Official) | Tools: 8 | Setup: Connection string

Most Spring Boot applications use JPA with Hibernate to interact with a relational database, and most of the time that database is PostgreSQL. The PostgreSQL MCP server gives your AI assistant direct access to your database, which transforms how it helps you write and debug JPA code.

What It Does

Eight tools for running SQL queries, inspecting schemas, listing tables, describing columns and constraints, and analyzing query execution plans. The assistant can see your actual database structure rather than inferring it from your entity classes.

How It Helps in Practice

JPA mappings are one of the most common sources of bugs in Spring Boot applications. You have a @OneToMany relationship between Order and OrderItem, but the cascade type is wrong and orphan removal is not set correctly. Instead of running the application, triggering the issue, and reading through a Hibernate exception that spans 40 lines, your assistant can inspect the actual table structure and foreign keys in PostgreSQL, compare them with your entity annotations, and identify the mismatch.

This is equally valuable for migrations. When you need to add a column to a table that has millions of rows, the assistant can check the current schema, examine existing indexes, and write a Flyway or Liquibase migration that avoids locking issues. It makes its recommendations based on the actual database state, not assumptions about what your schema looks like.

Query performance is another area where direct database access helps. Your assistant can run EXPLAIN ANALYZE on the SQL that Hibernate generates, identify missing indexes, and suggest the correct JPA query hints or native queries to fix the problem.

Configuration

{
  "mcpServers": {
    "postgres": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-postgres",
        "postgresql://localhost:5432/myapp"
      ]
    }
  }
}

Replace the connection string with your actual database URL. For production databases, use a read-only connection to prevent accidental modifications.

Docker MCP -- Containers From Your Editor

Author: Community | Tools: 14 | Setup: Zero-config (npx)

Spring Boot applications are almost always containerized for deployment, and Docker is the standard tool. The Docker MCP server lets your assistant manage your containers, images, volumes, and networks without you switching to a terminal or Docker Desktop.

What It Does

Fourteen tools for the full Docker lifecycle: listing and inspecting containers, starting and stopping them, viewing logs, managing images (pull, build, list, remove), and working with volumes and networks. Your assistant can see what is running, check container health, and read logs -- all from the same conversation where you are writing code.

How It Helps in Practice

Spring Boot applications typically run alongside several services in development: PostgreSQL, Redis, maybe Kafka or RabbitMQ. When something goes wrong, the first question is usually "is everything actually running?" Instead of opening a terminal and running docker ps, then docker logs on the relevant container, your assistant can check all of this in one step.

Multi-stage Docker builds are another area where the assistant's help is valuable. Spring Boot applications need a build stage (with Maven or Gradle and a full JDK) and a runtime stage (with just a JRE). Getting the layer caching right, choosing the correct base image, and configuring the JVM memory settings are all tasks where the assistant can inspect your current Dockerfile and container behavior, then suggest improvements based on what it sees.

When a container is misbehaving -- the application starts but health checks fail, or it runs out of memory -- the assistant can inspect the container's resource usage, read the logs, and correlate the issue with your application.yml configuration. It has the full picture without you copying anything.

Configuration

{
  "mcpServers": {
    "docker": {
      "command": "npx",
      "args": ["-y", "docker-mcp"]
    }
  }
}

No API keys required. The server communicates with the local Docker daemon.

Context7 MCP -- Spring Docs That Match Your Version

Author: Upstash (Official) | Tools: 2 | Setup: Zero-config (npx)

Spring Boot's API surface is enormous, and it changes meaningfully between major versions. The migration from Spring Boot 2.x to 3.x changed package namespaces (from javax to jakarta), deprecated numerous configuration properties, and restructured security configuration entirely. AI assistants trained on older data frequently suggest patterns that no longer work. Context7 MCP fixes this by pulling live documentation.

What It Does

Two tools: one to resolve a library name to a Context7 identifier, and one to query that library's documentation with a specific question. Context7 indexes Spring Boot, Spring Security, Spring Data JPA, Spring Cloud, and hundreds of other Java libraries.

How It Helps in Practice

You are configuring Spring Security in a Boot 3.x application. The old WebSecurityConfigurerAdapter approach was removed entirely in Spring Security 6. An assistant relying on training data might suggest the deprecated pattern, and you would not realize the error until the application fails to compile. With Context7 MCP, the assistant fetches the current Spring Security documentation and writes the configuration using the SecurityFilterChain bean approach that actually works.

This pattern repeats constantly in Spring development. You need to configure a new RestClient (introduced in Spring Boot 3.2) rather than RestTemplate. You want to use virtual threads (Spring Boot 3.2+). You need the correct way to define a @Bean method for a DataSource in the latest version. Every time, Context7 ensures the assistant is working with current documentation rather than memorized but outdated patterns.

For a framework as annotation-heavy as Spring, getting the exact annotation and its parameters right matters. @Transactional(readOnly = true, propagation = Propagation.REQUIRES_NEW) has very different behavior from @Transactional. Context7 helps the assistant understand these nuances by referencing the actual documentation.

Configuration

{
  "mcpServers": {
    "context7": {
      "command": "npx",
      "args": ["-y", "@upstash/context7-mcp"]
    }
  }
}

No API key required. At roughly 1,030 tokens for tool definitions, this server adds negligible overhead to your context window while dramatically improving the accuracy of Spring-related suggestions.

GitHub MCP -- PRs, CI, and Code Review

Author: Anthropic (Official) | Tools: 20 | Requires: GitHub personal access token

Java projects tend to have structured workflows: feature branches, code reviews, CI pipelines with build and test stages, and release management. The GitHub MCP server makes all of this accessible from your editor.

What It Does

Twenty tools for managing repositories, branches, pull requests, issues, GitHub Actions workflows, and code search. Your assistant can create PRs, check CI status, search code, open issues, and interact with your entire GitHub workflow.

How It Helps in Practice

You have finished implementing a service layer refactor that touches 15 files across three modules. Creating a meaningful PR description that explains what changed and why is tedious when you are doing it from memory in a browser tab. With GitHub MCP, the assistant already knows exactly what changed (it helped you write the code) and can create the PR with a detailed, accurate description that maps changes to modules and explains the reasoning.

When CI fails -- and in Java projects with comprehensive test suites, it does -- the assistant can check the GitHub Actions workflow status, retrieve the test failure output, and correlate it with the changes in your PR. This is especially useful for integration tests that fail due to database state issues or service interaction problems. The assistant can cross-reference the test failure with the actual database schema (via PostgreSQL MCP) and the container state (via Docker MCP) to pinpoint the root cause.

Code search is also valuable in large Java codebases. Spring Boot applications tend to grow into many packages with service classes, repository interfaces, configuration classes, and DTOs. The assistant can search across the repository to find every @Service class that injects a particular repository, or every endpoint that returns a specific DTO type.

Configuration

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "your-github-token"
      }
    }
  }
}

Sentry MCP -- Java Stack Traces Without the Noise

Author: Sentry (Official) | Tools: 8 | Requires: Sentry auth token

Java stack traces are notoriously long. A single NullPointerException in a Spring Boot application can produce a stack trace that spans 50+ lines, most of which are framework internals. Sentry captures these errors in production, and the MCP server lets your assistant query them directly.

What It Does

Eight tools for querying recent errors, viewing error details with full stack traces and breadcrumbs, analyzing performance data, listing and managing issues, and checking error frequency and affected user counts.

How It Helps in Practice

Your Spring Boot application is throwing intermittent LazyInitializationException errors in production. These are the classic Hibernate "could not initialize proxy -- no Session" errors that happen when you access a lazily loaded relationship outside of a transaction boundary. The stack trace is deep: it goes through your controller, through Spring's proxy layer, through Hibernate's session management, and into the entity itself.

Without MCP, you open Sentry in a browser, find the issue, scroll through the stack trace to identify the relevant lines (your code, not the framework frames), switch to your editor, find the offending code, and figure out the fix. With Sentry MCP, you ask the assistant about recent errors. It retrieves the stack trace, filters out the framework noise, identifies that the OrderService.getOrderWithItems() method is accessing order.getItems() after the transaction has closed, and suggests either moving the access inside the @Transactional boundary or switching to FetchType.EAGER for that relationship (with an explanation of the tradeoffs).

The breadcrumbs feature is particularly useful for Spring Boot. Sentry captures the HTTP request details, the SQL queries that were executed, and the timing of each operation. When the assistant has access to this data alongside your code, it can reconstruct the complete request lifecycle and pinpoint exactly where things went wrong.

Configuration

{
  "mcpServers": {
    "sentry": {
      "command": "npx",
      "args": ["-y", "sentry-mcp"],
      "env": {
        "SENTRY_AUTH_TOKEN": "your-sentry-token"
      }
    }
  }
}

The Stack -- Combining Everything

These five servers cover the essential layers of a Spring Boot development workflow:

1. Database: PostgreSQL MCP gives the assistant direct visibility into your schema, queries, and data.
2. Infrastructure: Docker MCP handles container management for your application and its dependencies.
3. Documentation: Context7 MCP ensures Spring-specific advice is current and version-accurate.
4. Source control: GitHub MCP integrates PRs, CI, and code search into the conversation.
5. Production monitoring: Sentry MCP surfaces errors with full stack traces and request context.

The servers complement each other in ways that single-tool workflows cannot match. When an error surfaces in Sentry, the assistant can check the database schema in PostgreSQL to verify the entity mapping, pull up the relevant Spring Data documentation in Context7 to confirm the correct pattern, and create a GitHub issue with the full context. That is three tool switches compressed into one conversation.

Getting Started

Start with the servers that address your daily friction:

• Fighting JPA mapping issues? PostgreSQL MCP gives the assistant direct schema access. Start here.
• Getting outdated Spring suggestions? Context7 MCP is zero-config and fixes this immediately.
• Spending time debugging containers? Docker MCP keeps your dev environment visible.
• Want error context without browser-switching? Sentry MCP brings production errors into your editor.
• Need better CI integration? GitHub MCP makes PRs and pipeline status part of the conversation.

The total token budget for this stack is approximately 26,780 tokens, which is modest enough to run comfortably alongside your code in any modern context window.

For the complete pre-configured stack, visit stackmcp.dev/stacks/java-spring. Select your AI client, enter your connection details, and copy the generated config.