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"Spring Boot" matching MCP servers:

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    MCP server for Java/Spring Boot architecture quality analysis and PR review, providing tools like detect_spring_context, review_pr_diff, and scan_violations.
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    MIT
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    A secure-by-default MCP server and CLI for AI agents to inspect Spring Boot repositories and interact with runtime Actuator endpoints, enabling code review, dependency scanning, and monitoring.
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    MIT
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    An MCP server that ingests Spring Boot REST microservices from GitHub, builds a request-flow ontology, and enables natural-language queries about code with flow-aware answers and citations.
    MIT
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    Enables AI tools to collect a compact, structured context bundle for a Spring Boot + MyBatis screen by walking the dependency graph from a route, controller, or file.
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    Framework-aware code intelligence MCP server that builds a cross-language dependency graph from source code. 53 integrations (Laravel, Django, Rails, Spring, NestJS, Next.js, and more) across 68 languages. 100+ tools for navigation, impact analysis, refactoring, security scanning, session memory, and CI/PR reports — up to 97% token reduction.
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    MIT
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    Encodes a TypeScript/JavaScript codebase into a deterministic PNG heatmap that vision-capable AI models can read to bypass token limits for codebase-scale audits.
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    PolyForm Noncommercial 1.0.0
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    Analyzes API codebases from GitHub and Bitbucket repositories to generate Postman collections, business reports, and detailed code insights. Supports multiple frameworks including FastAPI, Spring Boot, Flask, Express, and OpenAPI/Swagger specifications.
    MIT
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    Umbra is the trust layer for AI-generated code. It scores any repo 0-100 with file:line evidence (security, slop, Docker-verified build/boot, and claim receipts that catch agents lying about tests), and guards agent file writes mid-stream blocking leaked keys, alg:none JWTs, and git-hook plants before they land. Tools: scan_repo, guard_content, get_score.
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    1,570
    MIT
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    A navigable symbol map of your codebase for coding agents, enabling efficient code navigation and reading only relevant symbols instead of entire files.
    MIT
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    RegexForge gives AI agents a reliable way to get a regex without asking an LLM to hallucinate one. Pass in labeled examples (strings that should match, strings that shouldn't) plus an optional description; get back the regex, a proof matrix showing it handles every example, and a backtracking-risk audit flagging catastrophic-backtracking patterns. Pure symbolic synthesis over a template bank with