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

Gemini-powered MCP server for automated code review, analysis, and documentation.

Educational MCP server with 17 tools for mathematical calculations, matrix operations, statistical analysis, and data visualization. Includes persistent workspace, cloud hosting, and safety features like restricted eval.

A production-grade MCP server for the Code-Fundi API, enabling AI agents to map codebases, search semantically, and analyze blast radius. It provides tools for repository management, AI-powered research, and impact analysis before shipping changes.

This MCP server enables remote control and management of Claude Code agents, allowing you to execute missions, configure agent personalities, and integrate with other MCP tools.

A small, readable MCP learning project implemented in TypeScript that uses a local JSON notebook to demonstrate core MCP features such as tools, resources, prompts, sampling, and notifications.

A production-grade, extensible Python template for building Model Context Protocol servers with support for Streamable HTTP and stdio transports. It provides a structured framework for implementing tools, resources, and prompts with built-in authentication, observability, and background task management.

Enables writing to Figma from MCP clients like Claude Code without a Figma Dev seat, using a local WebSocket relay and a Figma dev plugin.

An MCP server that integrates AI agents with Figma, enabling reading and programmatic modification of designs.

A SitecoreMCP version that can be used in enterprises

A lightweight MCP server that enables AI assistants to execute local development tools and retrieve system status with low latency over stdio or HTTP.

MCP server for overseer.nvim — gives any MCP-capable coding agent running inside Neovim (Claude Code, Codex, Gemini CLI, opencode) the same visibility and control over tasks the user has: list, run, tail, restart, stop. Agent-started dev servers land in the user's own task list instead of becoming invisible orphaned processes.

Enables AI models to access and search offline Wikipedia and other knowledge bases stored in ZIM format files. Provides intelligent content retrieval, structured browsing, advanced search capabilities, and metadata extraction for comprehensive offline knowledge access.

An offline MCP server for SAP ABAP that provides static analysis, ABAP Cloud readiness checks, and RAP scaffolding using abaplint, enabling AI agents to analyze and generate ABAP code without a live SAP system.

Live Japan seasonal travel data from Japan Meteorological Corporation. 12 tools covering cherry blossom (sakura) forecasts, autumn leaves (koyo), fruit picking, flowers, festivals, and 3-day weather. 1,700+ GPS-tagged spots updated daily at 9AM JST. Works via npx or hosted HTTP endpoint — no auth required.

A TypeScript-based server that enables programmatic management of pseudo-terminal sessions. It allows users to spawn, control, and interact with terminal processes in real-time through the Model Context Protocol.

Enables AI assistants to search live jobs, view full details, save and track applications in a Kanban board, set follow-up reminders, and subscribe to job alerts—no account needed for searching.

Standalone MCP server for Obsidian vaults - hybrid search (FTS5 + vector + cross-encoder reranking), images and PDFs in agent-readable form, Kanban-aware tasks (Tasks-plugin + Dataview formats), structured memory with topic recall, fine-grained read/write tools for optimal token efficiency, and link graph support. Run locally, self-host, or one-click deploy for remote access. OAuth 2.1.

Enables local SOC 2 compliance work by scanning Infrastructure-as-Code, mapping findings to SOC 2 Trust Services Criteria, explaining risks in plain English, and drafting audit-ready documents without uploading files.

Fetches YouTube video transcripts, using free captions when available and falling back to Gemini-based transcription for missing or broken captions.

The MCP server for Nanostores that scans your codebase with AST analysis to find stores, subscribers, and dependency chains, and can ingest live events to spot noisy, error-prone, or unused stores.