mcp-spring-boot-actuator
MCP server for Spring Boot Actuator analysis — health, metrics, beans, and environment diagnostics
Documentation
MCP Spring Boot Actuator
An MCP server that analyzes Spring Boot Actuator endpoints — health, metrics, environment, beans, startup, and caches. Detects issues, security risks, and provides actionable recommendations.
Why This Tool?
There is no other MCP server that analyzes Spring Boot Actuator endpoints. This is the only tool that lets your AI assistant understand your Spring Boot application's health, performance, configuration, and startup behavior through actuator data.
7 analytical tools turn raw actuator JSON into actionable diagnostics — health checks, JVM metrics analysis, security risk detection in environment/beans, startup bottleneck identification, and cache efficiency analysis.
Pro Tier
Generate exportable diagnostic reports (HTML + PDF) with a Pro license key.
- Full JVM thread dump analysis report with actionable recommendations
- PDF export for sharing with your team
- Priority support
$9.00/month — Get Pro License
Pro license key activates the `generate_report` MCP tool in mcp-jvm-diagnostics.
Tools (7)
`analyze_health`
Parse and diagnose the `/health` endpoint response. Detects unhealthy components (database, Redis, Kafka, Elasticsearch, disk space) with component-specific recommendations.
curl http://localhost:8080/actuator/health | jq '.' > health.jsonDetects:
- DOWN/OUT_OF_SERVICE components
- Low disk space warnings (= 90% (CRITICAL) or > 75% (WARNING)
- High thread count (> 500)
- Long GC pauses (avg > 200ms)
- HTTP error rate > 10% (CRITICAL) or > 1% (WARNING)
- Connection pool exhaustion >= 90% (CRITICAL)
- Pending connection requests
Supports both flat metric values and Spring Boot measurement format (`{ measurements: [{ statistic: "VALUE", value: N }] }`).
`analyze_env`
Analyze the `/env` endpoint for security risks and misconfigurations.
curl http://localhost:8080/actuator/env | jq '.' > env.jsonDetects:
- Exposed secrets (passwords, API keys, tokens not masked with `******`)
- Risky production configs: `ddl-auto: create-drop`, H2 console enabled, `show-sql: true`
- DevTools enabled in production
- All actuator endpoints exposed (`management.endpoints.web.exposure.include=*`)
- Missing Spring profiles (no active profiles set)
`analyze_beans`
Analyze the `/beans` endpoint for architectural issues.
curl http://localhost:8080/actuator/beans | jq '.' > beans.jsonDetects:
- Circular dependencies (A → B → A)
- Singleton beans depending on prototype-scoped beans (scope mismatch)
- Beans with > 10 dependencies (God objects)
- Large bean counts (> 500)
- Multiple application contexts
`analyze_startup`
Analyze the `/startup` actuator endpoint (Spring Boot 3.2+). Parses the startup timeline to detect slow bean initialization and heavy auto-configurations.
curl -X POST http://localhost:8080/actuator/startup | jq '.' > startup.jsonParameters:
- `json` — The `/startup` endpoint JSON response
Detects:
- Slow startup (> 30s CRITICAL, > 15s WARNING)
- Heavy auto-configurations consuming > 30% of startup time
- Slow bean initialization (> 2s per bean)
- Top slowest steps ranked by duration
`analyze_caches`
Analyze the `/caches` actuator endpoint. Lists registered caches and detects configuration issues.
curl http://localhost:8080/actuator/caches | jq '.' > caches.jsonParameters:
- `json` — The `/caches` endpoint JSON response
Detects:
- Unbounded `ConcurrentMapCache` usage (no eviction, will grow indefinitely)
- Too many caches (> 20, memory overhead)
- Missing cache managers (no Spring Cache configured)
- Empty cache registrations
`analyze_loggers`
Analyze the `/loggers` actuator endpoint. Detects verbose logging configurations that impact performance and security in production.
curl http://localhost:8080/actuator/loggers | jq '.' > loggers.jsonParameters:
- `json` — The `/loggers` endpoint JSON response
Detects:
- ROOT logger set to DEBUG/TRACE (floods logs, degrades performance)
- Explicitly configured DEBUG/TRACE loggers (likely leftover from debugging)
- Verbose framework logging (Spring, Hibernate, HikariCP, Micrometer, Apache)
- Inconsistent log levels across related packages
- More than 5 verbose loggers (signs of leftover debug configuration)
Installation
npm install -g mcp-spring-boot-actuatorOr use directly with npx:
npx mcp-spring-boot-actuatorConfiguration
Claude Desktop
Add to your Claude Desktop config (`~/.claude/claude_desktop_config.json`):
{
"mcpServers": {
"spring-boot-actuator": {
"command": "npx",
"args": ["-y", "mcp-spring-boot-actuator"]
}
}
}Quick Demo
Once configured, try these prompts in Claude:
1. "Check the health of my Spring Boot app: [paste /actuator/health JSON]" — Detects DOWN components, low disk space, and provides component-specific recommendations
2. "Are there any security risks in my config? [paste /actuator/env JSON]" — Finds exposed secrets, risky settings like `ddl-auto: create-drop`, and over-exposed endpoints
3. "How is my app performing? [paste JVM/HTTP metrics]" — Analyzes heap usage, GC pressure, HTTP error rates, and connection pool utilization
- "Why is my app starting so slowly?" (paste `/actuator/startup` JSON)
- "Are my caches configured properly?" (paste `/actuator/caches` JSON)
Requirements
- Node.js 18+
- Spring Boot application with Actuator endpoints enabled
Part of the MCP Java Backend Suite
- mcp-db-analyzer — PostgreSQL/MySQL/SQLite schema analysis
- mcp-jvm-diagnostics — Thread dump and GC log analysis
- mcp-redis-diagnostics — Redis memory, slowlog, and client diagnostics
- mcp-migration-advisor — Flyway/Liquibase migration risk analysis
Limitations & Known Issues
- Actuator endpoints must be exposed: Spring Boot secures actuator endpoints by default. You must explicitly expose endpoints via `management.endpoints.web.exposure.include`.
- Startup analysis: Requires Spring Boot 3.2+ with `management.endpoint.startup.enabled=true`. Older versions don't provide startup timing data.
- Single instance: Analyzes one application instance at a time. For clustered applications, point to each instance separately.
- Metric accumulation: Some metrics (HTTP request counts, error rates) require traffic to accumulate data. A freshly started app may show zeros.
- Environment masking: Spring Boot masks sensitive properties by default. The `analyze_env` tool sees masked values (e.g., `******`) and cannot detect actual credential exposure in masked properties.
- Custom health indicators: The tool recognizes standard health indicator patterns. Custom health indicators with non-standard status values may not trigger specific recommendations.
- Cache analysis: Supports ConcurrentMapCache, Caffeine, Redis, and EhCache. Other cache providers may show limited analysis.
- Non-JSON responses: Handles HTML error pages (401, 403, 500) gracefully with "Invalid JSON" warnings, but cannot extract useful data from them.
- Circular dependency depth: Detects cycles of any length, including multi-hop chains (A→B→C→A).
License
MIT
End-of-life: 2026-05-10.
This MCP server is no longer maintained or distributed. The Corporation
has pivoted to Apify marketplace actors. See
`irrcorp/bzp-poland-tenders` for current Corporation work.
The npm package has been unpublished. The repository is archived for
historical reference only.
Frequently asked questions
What is mcp-spring-boot-actuator?
mcp-spring-boot-actuator is MCP server for Spring Boot Actuator analysis — health, metrics, beans, and environment diagnostics
How do I install mcp-spring-boot-actuator?
Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.
Is mcp-spring-boot-actuator open source?
Yes — it is hosted on GitHub at https://github.com/Dmitriusan/mcp-spring-boot-actuator and has 1 stars.
Related MCP tools
The go-to web for your AI coding agent — local-first search, fetch, crawl & research over MCP. No API keys, no cloud, $0/query. Public beta.
MCP server that enables AI assistants to interact with Google Gemini CLI, leveraging Gemini's massive token window for large file analysis and codebase understanding
superglue (YC W25) builds integrations and tools from natural language. Get production-grade tools for long tail and enterprise systems.
Universal AI context generator. Saves thousands of tokens per conversation in Claude Code, Cursor, Copilot, Codex, and more.
A desktop MCP client designed as a tool unitary utility integration, accelerating AI adoption through the Model Context Protocol (MCP) and enabling cross-vendor LLM API orchestration.
Code research platform for AI agents; find, understand, and prove context across your code and all of GitHub, in a fraction of the tokens. One toolset, MCP or CLI
Run your own MCP server? See who uses it and what to fix.
Measure it with TrackMCP