"An MCP for creating games and animated apps" matching MCP servers:

Publishes a spending-summary tool alongside an interactive HTML view, letting people click a category in the answer to fetch the individual expense records behind that total. Keeps calculations and their supporting evidence together in a single inspectable flow.

A demo MCP server that returns an interactive dashboard (bar chart with dataset selector) as an MCP App via the official MCP Apps extension (SEP-1865).

Enables AI agents to search Iranian Android app stores, Cafe Bazaar and Myket, and retrieve app metadata such as version, rating, installs, size, update date and price, plus side-by-side comparisons of the same package across both stores and per-category top or newest listings. It is read-only and metadata only, returning compact JSON with store page URLs and supporting both Persian and English queries.

One local MCP server for Mail, Calendar, Reminders, Notes, Messages and five more macOS apps. Searches return pointers, not full bodies; deletes, moves and sends preview first; sending is off by default.

420+ deterministic fintech tools - agentic payments (AP2, x402, Visa TAP, A2A), AML/KYC, BaaS comparison, MCP dev tooling - with 15 flagship tools as interactive MCP Apps widgets. Read-only, no auth, zero PII.

An open engine that gives AI persistent, reusable UI components with a writable component registry, versioned data storage, and a shell runtime, enabling AI to create, save, and reuse single-purpose apps.

A demo MCP server showcasing interactive HTML UIs for tools like list sorting, flame graphs, and feature flags, enabling bidirectional communication between user and agent.

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.

Provides interactive, React-based UI apps inside Claude Desktop for tracking workouts, planning trips, budgeting, comparing products, and making decisions with bidirectional communication.

Enables AI agents to debug and repair historical Solaris/SPARC systems by providing an out-of-band control plane with guest DTrace, QEMU monitor access, SPARC-aware GDB, host eBPF/perf tracing, and an immutable evidence ledger for cross-layer hypothesis testing.

Enables AI agents to perform local video, audio, and file operations inside an isolated workspace, including cutting/concat videos, extracting audio, transcribing, and managing files, with typed responses and background job support.

Gives LLM agents spatial awareness inside Autodesk Maya, enabling scene inspection and measurement, aesthetic and engineering audits, camera planning, checkpoint/rollback safety, asset import, and viewport or render previews. Agents can also execute arbitrary Python in Maya to build and verify 3D scenes against engineering rules instead of writing blind.

Unofficial Powershell MCP server

Provides AI assistants with specialized tools to interact with NIST's Open Security Controls Assessment Language (OSCAL) framework. It enables agents to retrieve schemas, explore models, and generate valid OSCAL documentation for security compliance automation.

Enables async-first working practices by providing tools to draft decision docs, convert meetings to async artifacts, score status updates, and triage sync vs async tasks. It also offers reference tools for the Open and Async book's principles and coaching prompts.

MCP server that bridges Ableton Live with AI models, enabling real-time project inspection and control such as track overview, device parameters, and audio analysis.

Enables AI-driven 3D modeling and scene creation in Blender, allowing LLMs to create, modify, and manipulate objects, materials, and assets through natural language.

Enables LLMs to interact with Kibana through composable toolboxes for building dashboards, managing data, and handling alerts, among other operations.

Enables AI coding agents to discover and verify current open-source libraries and models using live GitHub activity, PyPI adoption, license checks, and hardware VRAM estimates before generating code.