"Python Object-Oriented Programming Resources" matching MCP servers:

Medicare claim documentation lookup for CPT codes against live CMS sources, returning structured requirements and document URIs with optional enrichment via RCI APIs.

Inspect, compare and export point clouds, meshes and calibrated depth data through MCP, with interactive inline 3D previews and PNG captures. Supports camera controls, selections, measurements, object transforms and animation playback.

A batteries-included Python template that ships a shared service registry as a CLI, MCP server, and HTTP API, enabling rapid development of MCP tools like the included Gmail skill.

Enables chat assistants to insert graded coding exercises (Python, JavaScript, TypeScript, SQL) with hidden tests, and returns failed attempts to the conversation so the tutor can explain the learner's specific mistake.

A MCP Server that lets user ask AI models to discover the collection of the Metropolitan Museum of Art. Adds the discovered art works as Resources on the server.

Enables MCP clients to screen lab results against published criteria with deterministic Python, search Australian contaminated-land guidance with cited passages, and draft guarded results sections whose numbers and citations are validated. All four tools are read-only and keep figures and citations under code control for a qualified person to check.

Creates Drake-style rap bars from any situation you type, and provides a complete MCP example with tools, resources, prompts, and an interactive UI widget.

An MCP server that provides live status updates for the Eco via Sirens game server, displaying meteor countdowns, player statistics, and world information directly within Claude Desktop. It also serves as a minimal reference implementation for building MCP Apps in Python without complex tooling.

An MCP server for rendering common ECharts visualizations from structured tool arguments, exposing per-chart opener tools that map to independent HTML app resources.

Integrates Agent Skills into LLMs by discovering skills in a configured directory and progressively loading their instructions and resources on demand through a structured disclosure pattern.

Enables an MCP-compatible AI client to author Blender Python, run Blender, import the resulting asset into Unity with materials and a prefab, place it in the active scene, verify the import, and revise the same asset using stable IDs.