VectorClaw v1.0.0: MCP Server for Anki Vector Robot Control

VectorClaw v1.0.0 is an MCP server that lets OpenClaw control a physical Anki Vector robot. The Vector is a palm-sized robot with tank treads, a lift arm, HD camera, proximity/cliff sensors, touch sensor, speaker, screen face, and WiFi connectivity that can operate cloud-independently with Wire-Pod.
Available Tools
The server provides 23 MCP tools organized into functional categories:
- Speech:
vector_say - Motion:
vector_drive,vector_head,vector_lift - Perception:
vector_look,vector_capture_image - Sensors:
vector_proximity_status,vector_touch_status,vector_pose - Display:
vector_face
How It Works
This enables embodied AI through tool orchestration. Your agent can perceive → reason → act in a loop:
vector_look() → capture image Send to vision model → "What do you see?" Agent decides action based on response vector_drive() or vector_say() → act
The approach allows your assistant to control a physical platform by calling tools rather than requiring a specialized robotics model.
Roadmap
v1.x planned features:
- Async motion control (non-blocking drive with interrupt capability)
- Audio recording from robot microphones
- Multiple robot support
- Personality modes
v2.0 vision: ROS2 integration for SLAM + NAV2, enabling higher-level commands like "go to kitchen" instead of low-level "turn 90°, drive 2m" instructions.
Links
- PyPI: https://pypi.org/project/vectorclaw-mcp/
- ClawHub: https://clawhub.ai/danmartinez78/vectorclaw-mcp
- GitHub: https://github.com/danmartinez78/VectorClaw
📖 Read the full source: r/openclaw
👀 See Also

OpenClaw Skill Server for Indian Market Analysis and Trading
An open-source trading terminal for Indian markets has been integrated as an OpenClaw skill server, allowing agents to pull market data and run multi-agent analysis via HTTP. The system provides structured trade plans with entry prices, stop-losses, and targets across three risk profiles.

Utilyze: Open-Source GPU Monitor That Measures Real Compute Throughput, Not Just Kernel Activity
Utilyze samples hardware performance counters to report compute and memory throughput relative to theoretical limits, exposing that dashboards showing 100% utilization may have only 1-10% real throughput.

Reverse Engineering Apple Neural Engine for Training MicroGPT Models
A developer reverse engineered Apple's Neural Engine private APIs to create a training pipeline for a 110M parameter MicroGPT model, achieving 6.6 TFLOPs/watt power efficiency on M4 Mac hardware.

Corey Haines' Marketing Skills Set for AI Agents
A set of 25 marketing skills for AI agents has been added to OpenClaw, covering conversion optimization, copywriting, analytics, and growth engineering. The conversion optimization skill is noted as particularly effective in multi-agent setups.