okabebot-mcp
Related Servers
Alternatives to okabebot-mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityBmaintenanceEnables AI agents and humans to control various hardware boards (ESP32, Arduino, Raspberry Pi, etc.) through a unified API via MCP, with real-time dashboard support.MIT
- AlicenseNot gradedqualityDmaintenanceA Model Context Protocol (MCP) server for seamless integration with peripheral devices connected to your computer. Control, monitor, and manage hardware devices through a unified API.5MIT
- AlicenseNot gradedqualityBmaintenanceA unified MCP bridge server that lets WorkBuddy and any MCP client control real-world hardware over MQTT and HTTP — reading device state and sending commands through natural language, with a pluggable adapter layer for adding new protocols.MIT
- AlicenseNot gradedqualityBmaintenanceEnables MCP clients to drive a Stack-chan desk robot over the network, exposing tools for photos, audio recording, speech, head movement, LED control, and touch events.Apache 2.0
- AlicenseAqualityBmaintenanceEnables MCP-capable agents to see and control robots directly by listing tasks, starting and resetting episodes, observing camera feeds and robot state, and moving end effectors and grippers, including coordinating two arms. Supports LIBERO, RoboDojo, a simulator-free fake backend and remote backends over SSH or TCP, with configurable episode budgets, logs, live monitoring and frame-by-frame replay.737 PyPI5Apache 2.0
- AlicenseNot gradedqualityCmaintenanceEnables cloud LLM agents to discover and invoke physical hardware on edge and IoT devices through standard MCP tools, bridging constrained device channels like UART, BLE, and Wi-Fi.3MIT
TDQS
Scored across 39 tools
The set covers many distinct domains, but several closely related tools are easy to confuse: speak vs character_speak vs speak_on_echo, get_calendar_agenda vs show_calendar_agenda, and display/notification/card tools. Descriptions help clarify boundaries, but an agent still needs careful selection among similar speech, display, and notification actions.
Most tools follow a predictable snake_case verb_noun or noun_action pattern, such as set_speaker_volume, list_reminders, and get_device_health. There are minor deviations like speak, character_speak, character_expression, and display_message/display_image, but the overall convention is readable and consistent.
Thirty-nine tools is very large for a single assistant surface, spanning speech, display, camera, reminders, notifications, Spotify, calendar, focus, SwitchBot, and diagnostics. While many tools are meaningful, the volume creates a heavy selection burden and increases the chance of misselection.
Coverage is broad across most lifecycle areas: reminders can be created, listed, and cancelled; notifications can be published, listed, snoozed, dismissed, and configured; display, audio, camera, Spotify, calendar, focus, and SwitchBot operations are present. Minor gaps exist, such as no reminder edit/update and limited management for some captured media, but agents can work around them.