"Learning Programming" matching MCP servers:

Enables AI assistants to run code in 24 programming languages on HackerEarth's sandboxed servers and read the printed output or errors, without installing local toolchains. It also lists supported languages with exact identifiers for use when submitting code.

CodeSafer is an MCP server that scans AI-generated code for 9 categories of hidden security threats — including invisible Unicode, Trojan Source, homoglyphs, and rules file backdoors — using static analysis plus CodeBERT deep learning. Runs locally, free tier available.

Enables automated migration of JavaScript/TypeScript packages from legacy test runners like Karma, Webpack, and Mocha to Vitest, including AST codemods, multi-project test scaffolding, and self-learning diagnostics for resolving runtime errors.

Provides seamless access to the Fake Store API for AI assistants with 18 CRUD tools for managing e-commerce data including products, carts, and users. Perfect for e-commerce demos, testing, and learning MCP development with zero configuration required.

Detects and automatically removes AI-generated code slop patterns like redundant comments, verbose logging, and unnecessary error handling across multiple programming languages.

ModelSafetyMCP is an MCP server for scanning machine learning model artifacts for unsafe serialization, malicious model patterns, risky packaging, URL-based artifact scanning, and directory-level triage.

Provides intelligent error detection and debugging capabilities across multiple programming languages with real-time monitoring of build, lint, runtime, console, and test errors. Offers AI-enhanced error analysis with automated resolution suggestions and context-aware debugging.

Enables code execution in 70+ programming languages and databases through the Onecompiler API, allowing users to run and test code directly from their MCP-enabled applications.

Enables learning and integration testing of the Model Context Protocol by evolving from a minimal server toward production-grade tools, resources, prompts, and agent interoperability.

A multi-language testing automation server that enables AI assistants to discover, plan, and execute tests across six major programming languages. It provides comprehensive tools for repository scanning, automated test execution, and detailed coverage reporting.

Provides comprehensive code analysis through three MCP servers: static analysis for code quality and security, dependency analysis for package management and vulnerabilities, and complexity analysis for maintainability assessment across multiple programming languages.

Enables AI agents to directly inspect, configure, test, and orchestrate network devices through pyATS/Genie, including show command parsing, config rollback, feature state learning/diffing, fleet-wide command fan-out, and declarative test execution.

Showcases principal-level QA architecture using MCP tools and Playwright for validating security, business workflows, contract safety, and cross-layer consistency.

A Model Context Protocol tool for analyzing code repositories, performing security scans, and assessing code quality across multiple programming languages.

Enables Claude Code, Cursor, Codex, and other AI agents to safely drive a locally installed Xilinx Vivado through 32 tools spanning GUI/Tcl/attach sessions, synthesis, implementation, timing, CDC, XDC, IP, waveforms, and device programming. It adds structured timing evidence with baseline comparison, offline VCD queries, evidence-driven workflow prompts, and read-only environment self-diagnostics.

Enables AI agents such as Claude Code, Cursor, and Codex to drive a locally installed Xilinx Vivado through 32 curated tools spanning sessions, synthesis, implementation, timing, CDC, XDC, IP, waveform inspection, and device programming. It also provides structured timing evidence with baseline comparison, bounded offline VCD queries, environment self-diagnosis, and Chinese-language diagnostics for timing and CRITICAL WARNING issues.

An interactive programming quiz server that provides assessment tools across ten tech categories with three difficulty levels. It enables users to launch quiz interfaces, receive graded feedback with explanations, and request hints through MCP-compatible tools.