"Learning or Writing Code in Python" matching MCP servers:

Validates AI-generated Python: syntax, lint, security scan and deterministic repair.

Enables execution of Python code in a safe environment, including running scripts, installing packages, and retrieving variable values. Supports file operations and package management through pip.

A learning MCP server providing tools for OS info, file operations, weather, SQLite queries and resources for documentation and user profiles.

MCP server that provides OpenRouter model pricing data, enabling price lookups, trending/cheapest lists, and model searches without an API key.

An MCP server for securely executing Python code, supporting STDIO, Streamable HTTP, and SSE transports, with easy deployment on Heroku.

Extends the official Obsidian MCP server with intelligent learning and second-brain features, including AI-generated challenges, progress analytics, spaced repetition, and knowledge gap analysis.

Enables AI coding assistants to browse Unreal Python API documentation, search classes and functions, and execute Python code in Unreal Editor.

Content writing toolkit for headline generation, readability scoring, tone analysis, outline building, and plagiarism similarity checking.

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.

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.

A Model Context Protocol server for deep codebase understanding of Python projects, focusing on data analysis and scientific computing. It provides architectural analysis, pattern detection, dependency mapping, test coverage analysis, and AI-optimized context generation.

MCP server offering text analysis tools for writing improvement, including spellcheck, readability, keyword analysis, passive voice detection, and AI-generated content detection.

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.

Enables the AI Learning Assistant to perform web searches for current or external information, returning ranked public-web results with titles, URLs, snippets, and scores over Streamable HTTP.

Exposes Bilibili learning videos to MCP clients, letting them search videos, read descriptions and video metadata, fetch timestamped subtitles, and view top comments for note-taking and Q&A. All summarization is left to the client model, with optional cookie-based login for subtitle access.

An MCP server that enforces a pedagogical workflow to teach topics step by step, using research, decomposition, and spaced retrieval practice.

Call OpenAI Codex from Claude Code for independent second opinions, structured code review, and delegated coding tasks through a FastMCP plugin that drives the codex CLI safely.

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

Provides current weather data for any city using the Open-Meteo API, exposed as an MCP tool for integration with MCP clients.

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

Enables users to train and compare Logistic Regression and KNN models, make breast-cancer predictions, retrieve sample inputs, compute confusion-matrix metrics, generate SHAP explanations, select the best model, and export Parquet audit logs through MCP tools.

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

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

Enables educational and learning tasks including flashcard generation with Anki integration, Zotero library management, Obsidian vault interaction, and mathematical expression verification with LaTeX support.

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 users to describe their LLM fine-tuning job once and get the cheapest, fastest, and most balanced GPU options across a dozen cloud providers in seconds.

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.

A beginner-friendly repository for learning the Model Context Protocol (MCP) using FastMCP and uv, providing step-by-step setup and development workflow.

Enables agents to review Python dependency requirements and source code against a modernization catalog, returning structured guidance, import-level suggestions, and compatibility floors. It also lists available codemod recipes and previews their transformations as unified diffs, all read-only and without installing packages or executing supplied code.

A spaced-repetition study scheduler that syncs with Obsidian notes and uses SBB commute data to optimize study slots.

Enables LLMs to manage Python environments, execute Python code and scripts, install packages, and read or write files securely within a configured working directory.

Exposes a small catalog of programming study topics through the Model Context Protocol (MCP) for local learning.

Enables LLMs to run Python code and scripts, manage Python environments and packages, and read or write files within an isolated working directory. It also provides prompts for common Python tasks such as function creation, refactoring, and debugging.

Enables users to describe an LLM fine-tuning job once and receive VRAM/FLOPs-aware recommendations for the cheapest, fastest, or most balanced GPU options across a dozen cloud providers.

It enables users to describe an LLM fine-tuning workload once and receive task-aware VRAM/FLOPs estimates plus ranked cheapest, fastest, or balanced GPU recommendations across multiple cloud providers.

Enables LLMs to work with local Python environments by listing environments and installed packages, installing dependencies, and running code or scripts. Also supports reading, writing, and listing files within a sandboxed working directory.

Enables LLMs to manage Python environments and packages, execute Python code or scripts, and read or write files within a secured working directory. It also provides prompts for common Python tasks such as function creation, refactoring, and debugging.

Enables users to describe a fine-tuning workload once and receive task-aware VRAM/FLOP estimates plus ranked cheapest, fastest, and balanced GPU options across multiple cloud providers. It can be used from Claude Code/Desktop or CLI/API to identify viable hardware options without manually comparing provider pricing.

Enables LLMs to manage Python environments, execute Python code and scripts, install packages, and read or write files within an isolated working directory.