"Searching for personal use Minecraft Pocket Edition (MCPE) servers" matching MCP servers:

🔗 Model Context Protocol (MCP) Server for retrieving saved articles from Pocket API and loading them into Claude

Enables LLMs to read a user-owned, local-first memory store kept in a portable folder (USB, SSD or laptop), with per-profile permissions limiting what each client can see and access cut off when the pocket is locked or unplugged. Also lets models propose new memories for the user to review and approve, keeping provenance and keeping canonical memory free of direct model writes.

Enables bounded administration of Pocket ID, focusing on OIDC client management and read-only identity inventory, with secure handling of API keys and client secrets.

Enables AI agents to operate a local containerized Paper/Folia Minecraft test server, including lifecycle control, version switching, world rebuilds, RCON, plugin management, config read/write, and optional MCC bot slots.

Enables an AI assistant to keep every conversation verbatim in a local SQLite store and recall the most relevant past turns on demand, using hybrid keyword and embedding search plus time-awareness. Results are packed into a small token budget (2k–8k tokens) so long histories fit modest context windows, with secrets masked before storage.

Enables coding agents to query a local OTLP trace collector: list recent traces, inspect span trees and critical paths, flag SQL N+1 loops, surface gen_ai metrics, and diagnose slow traces with a local OpenAI-compatible model.

Enables an AI assistant to control a Sonicake Pocket Master guitar processor over USB MIDI by describing tones in plain words — selecting amp/cab/effect models, setting parameters, reordering the signal chain, and saving, renaming, backing up or restoring presets. It also provides offline tools for inspecting firmware and rebuilding it with a translated UI, such as Russian.

Launches multiple Browser-Use agents to automatically test websites for UI bugs, broken links, accessibility issues, and other technical problems on both live and localhost sites.

Lets AI assistants search every Pokémon TCG Pocket card by name, type, rarity, set, evolution stage, ability effect and attack, in six languages, with typo tolerance and semantic search.

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 small, readable MCP learning project implemented in TypeScript that uses a local JSON notebook to demonstrate core MCP features such as tools, resources, prompts, sampling, and notifications.

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.

Japan Operations OS for AI agents — 14 knowledge domains covering regulations, protocols, calendar, travel, food culture, language, disaster safety, daily life, and persistent memory. 31 MCP tools via REST + Streamable HTTP.

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

Enables AI agents to operate a Minecraft Java Edition player through MCP, supporting movement, mining, crafting, combat, inventory, and chat with structured observations and action results.

Provides comprehensive administrator-level control for Minecraft Java Edition servers, allowing AI to manage world generation, server configuration, and player moderation. It enables remote execution of RCON commands, NBT data parsing, and automated backup management through the Model Context Protocol.

Enables an agent to control a Minecraft Java Edition client through Mineflayer, supporting authentication, chat and slash commands, pathfinding and movement, combat, block and entity interaction, inventory and container management, world snapshots and queries, PNG captures, and an opt-in raw packet escape hatch.

Persistent memory storage using SQLite for AI agents to store, search, update, and manage contextual memory via the Model Context Protocol.

Allows AI models to observe and interact with the Minecraft world through a bot.

A set of MCP servers that allow AI assistants to control a Minecraft server and client, including running commands, managing plugins, taking screenshots, and calling arbitrary API methods via reflection.

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.

Browser control for Claude Code, on Firefox.

Enables AI agents to search and read source-grounded entries about Minecraft farms and contraptions, including inputs, outputs, labor steps, version applicability, and verification records. It provides two read-only tools and runs offline without accessing a game or executing builds.

An MCP server that lets an AI agent use a real terminal the way a person does: type, press keys, click, read the screen, take a screenshot.

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.

Runtime access for AI coding agents to observe and control Android and iOS apps.

Enables agents to build standard 64×64 Minecraft skins, validate technical texture properties, and generate inspection previews locally through MCP tools.

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

Enables LLMs to access a user's personal writing context—voice, style, opinions, expertise, projects, and communication patterns—via curated markdown files, helping the LLM match the user's voice when generating written content.