"Free and Easy-to-Use Minecraft Protocol Servers for Proof of Concept Projects" matching MCP servers:

Tamper-proof audit trail for AI decisions. 6 tools to record, verify, and export cryptographic proof chains via MCP.

Enables MCP clients to control a Minecraft survival character via Mineflayer, with a web UI, viewer, and custom TypeScript script execution.

Enables AI agents to control a Minecraft bot for movement, building, crafting, and instant schematic-based structure spawning via MCP tools.

Enables deploying pre-built AI agent blueprints for RFx procurement and employee onboarding, with no signup or API key required.

Enables AI agents to control a Minecraft bot inside a Java Edition world via MCP, providing tools for movement, building, mining, combat, farming, trading, inventory, and world perception.

A local MCP server that lets AI assistants control a Minecraft player on a local Paper server, enabling movement, block breaking/placing, combat, inventory management, chat, and op-level commands like teleport, gamemode, and summoning.

Exposes Minecraft server commands via RCON as a tool, and optionally powers an in-game AI chat listener that answers player questions using Claude.

A Minecraft MCP Server powered by Mineflayer API. It allows to control a Minecraft character in real-time, allowing AI assistants to build structures, explore the world, and interact with the game environment through natural language instruction

An MCP server that connects an AI assistant to a Minecraft server via a Mineflayer bot, supporting creative and survival modes with distinct toolsets.

An MCP server that enables AI agents to play Minecraft with full bot capabilities including pathfinding, block interaction, crafting, combat, and more.

Enables LLMs to control a Minecraft bot through the Mineflayer API, allowing for tasks like building, mining, and inventory management via natural language. It supports complex interactions including coordinate-based movement, block manipulation, and real-time game chat.

Enables MCP clients such as AstrBot, Claude, Cursor, or custom scripts to control a Minecraft bot over SSE through 49 tools covering perception, movement, building, gathering, crafting, combat, farming, memory, social interaction, and autonomous skills. It supports multi-client body control, emotion and event context, and survival/companion behaviors.

Enables AI agents to autonomously operate web browsers through the Model Context Protocol, including navigating pages, clicking, typing, filling forms, and extracting structured data.

Lets AI agents run verification policies, inspect structured proof bundles, and submit high-trust proof-of-work results to Gibwork through MCP tools. Works additively alongside existing Gibwork MCP setups, with optional policy, budget, and audit layers via a gateway.

Enables AI agents to control Minecraft bots through over 30 verified skills including navigation, resource gathering, and advanced building. It allows for multi-bot management and seamless integration with MCP clients to automate complex in-game tasks.

Provides cryptographic governance receipts for AI agents, enabling pre-execution evaluation and signed verdicts (EXECUTE/BLOCK/REVIEW/SHADOW) with offline-verifiable audit trails.

Enables AI models like Claude to control a Minecraft character through natural language commands, supporting movement, inventory, block interactions, and more.

Enables AI agents to control Minecraft bots via natural language commands by bridging a Python MCP server with a Node.js Mineflayer bridge. It supports a wide range of in-game actions including complex pathfinding, resource gathering, crafting, and combat.

Enables AI assistants to remotely control a Minecraft player, including navigation, mining, building, and interaction with the world and other players.

This server enables MCP clients to control a real Minecraft Java player client with native first-person gameplay, structured spatial perception, bounded continuous actions, and optional livestream widgets.

Local MCP server that lets AI agents control a Minecraft Forge 1.20.1 NPC to chat, move, gather, craft, build, fight, and manage inventory in a single-player world.

An MCP server implementing a multi-phase backend for structured model interactions, bounded compilation, exact-hash confirmation, and Codex-run provenance, with trust-separation hardening.

Local-first shared memory and coordination layer for AI coding agents, with repository evidence, reservations, handoffs, code graph context, and dashboard review backed by PostgreSQL/pgvector.

Trust, identity, and reputation infrastructure for AI agents. Register agents with W3C DID (Ed25519), check EigenTrust reputation scores, submit peer attestations, search agents by capability, and verify IPFS-anchored audit trails. 11 tools.

An open-source MCP server that lets any coding agent operate your computer like a person does—reading the UI through accessibility trees, clicking and typing in the background, showing an agent pointer, zooming into regions, and driving your signed-in Chrome on macOS, Windows, and Linux.

Enables MCP clients to submit AI agent claims for verification, read verdicts, and fetch or offline-verify signed receipts.

Control a real iPhone or iOS Simulator through a macOS daemon and XCTest runner over USB or Wi-Fi: read screen text, tap, type, swipe, inspect logs and replay flows with explicit outcomes. Browser sandbox: use readonly to inspect 12 preloaded flows without a phone. Reuse an existing sandbox from MCP Deployments, or delete it before starting another. Device control needs your Mac and iPhone.

An MCP server that provides AI trading agents with persistent, outcome-weighted memory to learn from historical performance and detect behavioral biases. It enables agents to automatically adjust strategies and optimize position sizing based on context-aware recall of past trade outcomes.

MCP server for local Mac desktop control, enabling mouse, keyboard, and screenshot actions for AI agents like Grok and Cursor.

Structured reasoning MCP server that decomposes problems into atomic steps (premise, reasoning, hypothesis, verification, conclusion) with confidence scoring, live visualization, and approval feedback.