"Eclipse Che" matching MCP servers:

An MCP server providing 63 semantic analysis tools for Java, built directly on Eclipse JDT for compiler-accurate code understanding.

A high-performance MCP server that bridges AI agents with Java codebases, providing professional-grade Java language intelligence via Eclipse JDT.LS.

Wraps the Eclipse JDT Language Server to enable AI assistants to understand Java codebases, search symbols, navigate definitions/references, and read third-party .class files.

Provides Java development capabilities through Eclipse JDT.LS, enabling symbol navigation, code diagnostics, workspace searching, and Javadoc access across Java projects.

Enterprise-grade code intelligence server for AI assistants that analyzes codebases for due diligence, tech debt, security vulnerabilities, and code quality.

Exposes Eclipse JDT Language Server to AI agents via MCP. Embedded OSGi architecture runs jdtls in the same JVM — no subprocess, no network hop. Tools: hover, definition, references, completion, diagnostics, document & workspace symbols.

MCP server for assisting in SAP ABAP CDS Views development on Eclipse ADT. Provides tools to generate, validate, and annotate CDS views, and suggest associations.

Enables agents to audit and safeguard repositories by detecting dependency pinning issues, license compliance problems, hardcoded secrets, and dead code through MCP tools.

An MCP server that lets AI models run code in 31 languages, evaluate symbolic math and logic problems, and measure complexity—exposed as 48 tools for execution, session management, translation, optimization, and more.

A read-only MCP server that provides AI assistants with structured access to SonarQube projects, issues, metrics, and rules. It enables safe analysis of code quality and security findings through a set of validated, safety-first tools.

Lets AI coding agents read an outline of a large file or a digest of noisy command output instead of the full text, while every hidden line stays retrievable on disk through a run handle. Agents can request only the line ranges, slices, or grep matches they need and list recent runs, cutting token spend without losing information.

A bounded MCP server for C++ analysis through Compiler Explorer, providing compiler discovery, compilation, assembly comparison, analyzers, and shortlink sharing.

Enables coding agents to audit a Next.js codebase read-only, checking routes, Prisma models, environment variables, and locale consistency via MCP tools.

An MCP server that gives AI assistants direct access to .NET test-coverage tooling, enabling them to run tests, parse Cobertura XML, identify uncovered branches, diff coverage between runs, and append test code.

MCP server for automatic Storybook story generation, component analysis, and validation. Auto-detects React frameworks and syncs component documentation.

A code graph that extends to any language via pluggable adapters, with task/QA traceability and real MCP tools — zero-config to try.

Exposes 11 agent-native QA tools over stdio so coding agents can discover a project, analyze risk, select the smallest relevant test set, generate and dry-run tests, triage failures, propose tiered heals, analyze flakes, generate quality reports, and evaluate release readiness. Planning and healing tools stay read-only or propose-only, while execution defaults to dry-run, keeping agent actions safe and evidence-backed.

Enables AI agents to search code, inspect symbols, navigate call graphs, and analyze change impact across scoped local repositories through the same engine as the CLI and REST API.

Enables auditable, plan-linked Git commit review views for Codex, with interactive file trees, split diffs, line-level explanations, and SHA-256 audit receipts served locally on localhost.

Exposes a set of CLI tools (test generation, documentation generation, linting, test running, code search) to AI assistants via MCP, allowing them to perform these tasks through natural language.