"Interactive and Fun MCP Servers for Online Gaming" matching MCP servers:

Provides phone intelligence for AI voice agents, enabling inbound caller identification, outbound risk assessment, line type detection, and do-not-contact checks via four read-only MCP tools.

interactive-mcp

A MCP server that collects interactive user feedback during AI-assisted development, reducing unnecessary tool calls and costs by enabling AI to confirm with users.

Enables AI agents to remotely control gaming PCs for automated gameplay with tools for screen capture, mouse/keyboard control, workflows, and system operations.

A powerful MCP server that provides interactive user feedback and command execution capabilities for AI-assisted development, featuring a graphical interface with text and image support.

Enables AI coding assistants like Cursor and Claude to engage in real-time two-way dialogue, allowing them to pause for clarification and request user confirmation before executing tasks. It features a modern UI for managing interaction prompts, predefined options, and project analysis to reduce errors and API costs.

Provides a Qt-based graphical interface for AI tools like Cursor to facilitate real-time human feedback and streamlined command execution. It features customizable shortcut responses, image processing, and a structured RIPER-5 workflow protocol to improve development accuracy and efficiency.

MCP server that enables human-in-the-loop workflow in AI-assisted development tools by allowing users to provide direct feedback to AI agents without consuming additional premium requests.

A Node.js MCP server that enables human-in-the-loop workflow for AI development tools, allowing users to run commands, view output, and provide textual feedback directly to AI assistants.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Enables Claude to discover new Solana meme tokens, assess their risk, inspect on-chain positions, and prepare or optionally execute buy/sell transactions under configurable spend and risk guardrails. It also logs plans and trades to Google Sheets for review.

A universal 'Ask User Questions' MCP tool for AI coding agents.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

Use Codex as a subagent inside Claude Code: async dispatch into a long-lived codex app-server, results pushed back into the Claude session automatically, steer/interrupt mid-task, and Codex's built-in code review.

Enables AI assistants to request user feedback and confirmation to prevent speculative tool calls and reduce resource usage. It streamlines task completion by consolidating multiple potential steps into a single, feedback-aware process.

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 Model Context Protocol server that enables AI assistants to request user feedback at critical points during interactions, improving communication and reducing unnecessary tool calls.

An AI-centric MCP server that enables automated Xilinx Vivado workflows, including project management, synthesis, implementation, and timing analysis. It allows AI agents to drive hardware design processes while integrating directly with the official Vivado GUI for visual context.

A local MCP server that lets Hermes supervise Claude Code, delegating focused coding, research, or review tasks to the Claude Code CLI and managing worker sessions, background jobs, cancellations, and read-only reviews.

Uses Claude as an independent second-model reviewer inside Codex, performing reviews on disposable Git snapshots with structured findings.

Enables AI agents to control the Unity Editor through MCP, allowing scene building, runtime scripting, visual QA, and more.

MCP server for step-by-step mathematical reasoning and planning, enabling AI agents to execute calculations and perform GUI actions like opening PowerPoint.

Lets a coding agent hand a plain-language app idea to a hosted AI software team that shapes, designs, builds, tests and deploys it, returning a one-time link into the full build workspace. Also lists your apps with their stage, pending approvals and live addresses after a standard OAuth sign-in.

Connects AI assistants to Interactive Brokers for intelligent portfolio management, options analysis, risk monitoring, and automated trading strategy suggestions. Enables real-time account tracking, Greeks calculations, option chain analysis, and playbook-based risk adjustments through natural language.

Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.

Windows computer use in the background for Antigravity and Gemini: look at a window, then act in one verified batch with targets named by their label, without taking the user's mouse, keyboard or foreground; irreversible actions need explicit confirmation.

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.

TheWeave is a markdown-native memory architecture for Claude and any MCP-aware agent. Your agent's memory lives as plain .md files you own: a 5-verb MCP core over the vault, query-driven PageRank retrieval, bi-temporal facts (valid_from / valid_until), and a persona-as-vault model. No database and no embeddings server. The files are the memory, inspectable in your editor and versionable in git.

Enables your own ChatGPT to build and manage a collaborative work board—dashboards, categories, datasets, monitors, and policies—through typed WebMCP tools, with human approval, audit trails, and guardrails enforced on every action.

Enables AI trading agents to register, submit and verify committed trades, and view their performance percentiles against 2,048 coin-flipping shadow agents.