"Resources for Learning Python Coding" matching MCP servers:

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

Web scraper and token compressor that converts HTML to clean markdown with 70-80% fewer tokens. Single-page compression and multi-page BFS crawling with auto-fallback fetch modes.

Enables developers to record feature-based development processes and turn Git changes, AI conversations, token usage, technical decisions, and debugging attempts into structured Obsidian learning reviews.

A learning-focused MCP server that demonstrates how to build arithmetic tools for AI assistants, currently featuring addition functionality with structured input/output.

Evidence-governed coding agent memory and knowledge lifecycle MCP server for DeepSeek Harness (DSH), OpenAI Codex, and Google Antigravity. Provides local SQLite FTS5 + semantic hybrid retrieval, automatic session/compaction checkpointing, counter-example demotion, and a hard 1,200-char context budget guardrail with zero third-party Python dependencies.

MCP server for coding-os, a cognitive operating system that gives AI agents memory, structure, and discipline. It exposes tools for project management, task tracking, knowledge graphs, and cognition traces to Claude Code and Codex.

Enables AI agents to index, search, and validate lessons learned across project repositories via a local SQLite index, providing learning-loop resources and verified-solution tracking without loading full files into conversations.

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

Provides AI coding assistants with code search, impact analysis, task chaining, and persistent memory to turn AI demos into deliverable code.

Guides users to official WCAG and W3C resources for learning web accessibility, without providing its own explanations.

AI learning assistant for families. Manages game time rewards based on school grades, homework, and bonus tasks. Syncs with EduPage/PRONOTE. 78 MCP tools with instruction workflows.

Provides safe repository operations, skill reading, command execution, and test detection for coding agents over stdio.

Turns your PDFs into locally stored flashcards and quizzes, grades your answers, and schedules spaced-repetition reviews entirely on your machine.

Execute Python code via subprocess with timeout and error handling, returning stdout, stderr, and exit code.

Enables supervising and orchestrating local coding agents like Muse and AGY through a client-agnostic MCP interface, with isolated Git worktrees, safe execution, and deterministic verification.

A beginner-friendly MCP server built in plain Node.js/JavaScript that exposes five tools (calculator, UUID generator, read notes, get weather, password generator) to teach how the Model Context Protocol works under the hood.

This MCP server provides direct access to ruff linting, formatting checks, and ty type-checking for Python projects, with token-efficient, structured output.

A local MCP server that helps you maintain a personal Japanese learning knowledge base, including vocabulary, confusion relations, mistakes, and spaced-repetition reviews. It provides tools and prompts for managing and reviewing your Japanese learning data without calling external LLM APIs.

Enables an AI assistant to author sentence-synced, motion-graphics lessons: it writes and validates lesson JSON, runs example code to verify output, renders 3D illustrations with Blender hotspots, synthesizes multi-voice narration with timing data, and builds a single-file HTML textbook. Supports any subject, from math and science to English and programming.

Enables delegating coding tasks to multiple AI CLI agents (Claude Code, Antigravity, Codex, Cursor) with session memory and diff inspection.

Python MCP server for SolidWorks automation with 109 tools covering the full CAD lifecycle. Enables AI-assisted design workflows through COM automation on Windows.

Enables external AI clients to invoke an autonomous coding agent that plans, fixes, and self-corrects code in an isolated Docker sandbox via run_task and run_tests tools.

Exposes the 23 Gang of Four design patterns to AI coding agents for generation, canonical examples, AST-based detection, validation, and anti-pattern refactoring in Python codebases.

Enables AI coding assistants to search a local codebase semantically using AI embeddings, finding relevant code by meaning instead of exact keywords. It also provides package version lookup, manual reindexing, cache clearing, workspace switching, and server health status tools.

Enables AI agents to develop within a local project workspace by reading and modifying files, running commands and tests, checking Git state, and persisting progress as history sessions that can be restored in later conversations.

Provides a local long-term memory layer for AI coding tools like Cursor and Claude Code, enabling cross-session, cross-tool sharing of project facts, user preferences, decisions, and workflows.

Enables AI coding assistants to find relevant code by meaning through local AI embeddings and an indexed codebase, so searches work even when terminology differs. It also supports package-version lookup, reindexing, cache management, workspace switching, and server status checks.

Enables AI coding assistants to search codebases by meaning using local AI embeddings, with tools for indexing, cache and workspace management, status checks, and latest package-version lookup.

Enables AI coding assistants to search codebases by meaning using local AI embeddings, and provides tools for package version lookup, reindexing, cache clearing, workspace switching, and status checks.

A task manager MCP server that demonstrates core MCP concepts (resources, tools, prompts, stdio) and syncs data with a Next.js web app.