"Resources to Learn and Understand C++ Programming" matching MCP servers:

Provides coding tutoring by running checks for common programming tasks (palindrome, prime, Fibonacci, GCD, etc.) and explaining the results in plain language.

An MCP server and CLI for step-by-step learning, enabling humans and agents to learn subjects like language tutoring through a structured prompt system.

Provides image understanding capabilities to coding models without vision support by automatically invoking a vision model and returning text descriptions, enabling seamless context-aware coding with images.

Enables AI assistants to deeply understand codebases via Knowledge Graphs, supporting fuzzy search, architecture layer queries, call chain tracing, impact analysis, and domain knowledge with multi-project support.

Provides 23 modular tools for competitive programming, including problem analysis, algorithm planning, code generation, verification, testing, code review, and learning assistance.

A Model Context Protocol server that enables Claude to execute Python code using boto3 to query and manage AWS resources directly from conversations.

MCP server that demonstrates the Resources feature by exposing static and dynamic resources, including contact data and personalized greetings, through MCP.

Enables AI assistants to browse your own university courses by downloading files, slides, announcements, deadlines, grades and lecture transcripts to your machine, then answering questions and searching across them through MCP tools. It logs in with your own account and refreshes only what changed.

Enables AI clients to search a personal corpus, read relevant source passages, and answer questions with traceable evidence from imported sessions, notes, and articles.

Enables AI agents to programmatically create and manipulate Qt-based visual programming flows, including device model parsing and port connection, through natural language.

Provides a standardized interface for interacting with Scikit-learn models and datasets, enabling training, evaluation, and model management through natural language.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

A simple MCP server that exposes a terminal tool, allowing AI agents to execute shell commands.

Serves plain-text notes that an Obsidian user has explicitly published for AI over streamable HTTP, with full-text and embedding-based search. MCP clients can read and search those notes and queue suggested edits that the user's own client applies only if the AI rules allow it.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

Enables AI agents to control a Live2D desktop pet's expressions and actions via MCP protocol.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

Enables semantic search over transcribed C++ lecture fragments with clickable YouTube timestamps, allowing users to retrieve and cite specific moments from the videos.