Official MCP server for Scorezilla (leaderboards for games). Your AI coding assistant can bootstrap a leaderboard, inspect games and boards, read top-N standings, and paste ready-to-run SDK integration code — without leaving the editor.
Social layer for AI coding — DMs, presence, discovery, and multiplayer games between developers. 68 tools including messaging, presence, memory, and 27 multiplayer games.
Local MCP server for the Spawn Games agent API, enabling LLMs to manage game projects, push updates, and interact with live games via a Playwright Chromium client.
Enables AI assistants to manage TapTap minigames and H5 games, including leaderboards, sharing, multiplayer, cloud saves, and game DC data query, with zero-config OAuth 2.0 authentication.
Validates a built HTML5 game against CrazyGames portal rules — build checks, an emulated SDK v3 session, and frame/layout audits — returning dense JSON lists of failures, warnings and rules it could not exercise instead of screenshots or ad-hoc Playwright scripts. It emulates the portal entirely in-process (no server or port) using the game's own Playwright/Chromium, with a small per-game scenario file driving gameplay steps.
Monitor failing GitHub Actions workflows, auto-fix them with OpenCode, and retry the run — driven by a workflow_run webhook, coordinated through an MCP server.
Enables AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.
A stdio-based MCP server that enables users to play and manage Minesweeper games through a Rails 8 REST API. It provides tools to start games, track states, and perform actions like opening cells and flagging mines.
Connect AI agents to your test results, insights, and targets. Query test runs, failures, flaky tests, and regressions across frameworks including Playwright, Jest, Pytest, Cypress and more.
Integrates ForgeaX game development into DeepSeek Harness by wiring the @forgeax/game MCP server, bundled Skills, and a workspace guard into DSH sessions.
Enables LLM clients such as Cursor and Claude to discover and use generated REST services through MCP tools for create, update, and delete actions and MCP resources for list and get-by-id context. Services generated in one command are exposed simultaneously as REST endpoints and MCP tools and resources.
Exposes generated CRUD services to LLM clients over the Model Context Protocol, providing tools for create, update, and delete actions plus read-only resources for listing and fetching records by id. It lets assistants like Claude, ChatGPT, and Cursor discover and manipulate the same data served by the project's REST endpoints without any extra glue code.
Exposes every generated CRUD service to LLM clients such as Claude, ChatGPT, or Cursor through discoverable MCP tools for create, update, and list actions plus read-only resources, so models can query and manipulate data without raw HTTP calls. It runs alongside the same codebase's REST endpoints, letting clients and AI share one data layer.
Enables LLMs to discover and invoke generated CRUD operations as MCP tools and read data through MCP resources, alongside standard REST endpoints, using one codebase.