"Resources for Learning Linux" matching MCP servers:

Provides educational tools for developers to learn programming concepts, review code, get best practices, compare approaches, generate learning paths, debug, explain code, and quiz themselves.

Helps AI assistants optimize Linux workloads on Arm64 by parsing perf report output, recommending NEON SIMD intrinsics for hot loops, and auditing Python dependency manifests for arm64 wheel availability — all offline and structured.

Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.

An MCP server for interacting with the Rell Language Server Protocol interface. It enables LLMs to query LSP Hover and Completion providers for Rell projects.

Enables AI agents to query Linux kernel source code, git history, LKML discussions, Patchwork patches, and documentation with evidence-ranked results, citations, and explicit assumptions or contradictions.

An MCP toolkit for algorithm learning that enables AI agents to search problems, generate step-by-step hints, analyze code, and create review templates for Baekjoon and Programmers problems.

Analyzes codebases to suggest relevant learning projects and generate interactive slides with direct links to the Marble platform. It enables developers to create tailored learning experiences based on their specific technologies, patterns, and code context.

A comprehensive MCP server providing arithmetic tools, dynamic file resources for frontend/backend documentation, reusable prompt templates for code review/debugging/testing, and complete development workflow management from requirements to deployment.

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.

Analyzes codebases for cross-platform compatibility issues, detecting platform-specific APIs, file path problems, line ending inconsistencies, and shell command incompatibilities across Windows, macOS, and Linux.

Enhances Claude AI with persistent memory storage for Infrastructure-as-Code components, supporting version tracking and relationship mapping for Terraform and Ansible resources.

Enables LLM-powered agents to generate, execute, and analyze Alloy modeling language code for software modeling and formal verification. Provides tools to run Alloy models, access documentation and examples, and generate models from system descriptions.

A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.

Enables MCP-compatible clients and agents to drive SpectraProbe's static analysis engine over stdio, exposing twelve tools for probing targets, inspecting metadata, strings, dependencies, symbols, imports, resources and JavaScript, searching evidence, creating reports, listing providers and checking analysis status.

Enables AI agents to assess AI-readiness and improve AI leverage through analysis tools, resources, and prompts for project scanning and remediation.

Enables agents to connect to Language Server Protocol servers as a headless editor, exposing LSP capabilities like diagnostics, rename, and formatting through MCP tools and resources.

Provides ChatGPT read-only access to inspect repositories, code, merge requests, pipelines, and CI logs on self-managed GitLab instances, including those reachable only from a private network or VPN.

Enables MCP clients to read only explicitly allowlisted Linux project files and selected Codex conversation turns. It supports listing projects and files, reading UTF-8 files, bounded text search, and paged reading of authorized conversations.

A local project intelligence, batch coding, and self-learning engine for Codex that enables task-level batch coding, reduces token usage, and automatically learns skills.