coppeliasim-mcp
Related Servers
Alternatives to coppeliasim-mcp
No user-submitted related servers found.
Related Servers
AlicenseAqualityDmaintenanceA local MCP server that connects Claude Code or any MCP-compatible AI assistant to a Robonine robot arm.1MIT- FlicenseNot gradedqualityCmaintenanceFull-access MCP server for the Webots robot simulator that enables AI assistants to see, understand, and modify a running simulation, including scene-tree inspection/editing, robot control, viewport screenshots, and arbitrary code execution.-
- AlicenseAqualityBmaintenanceAn MCP server that gives Claude (or any MCP-compatible AI host) read access to industrial sensor data and safety-gated control over motors and actuators.6MIT
- AlicenseAqualityAmaintenanceMCP server that lets Claude drive a real, running OrcaSlicer: load models, arrange the plate, tune settings, slice, and read results back.4445AGPL 3.0
- AlicenseNot gradedqualityAmaintenanceMCP server that lets Claude drive a running TouchDesigner instance to create operators, wire them, set parameters, run arbitrary Python, and introspect the td API.MIT
- AlicenseAqualityBmaintenanceA self-hosted MCP server that lets Claude (or any MCP client) drive Autodesk Fusion 360 on your own machine.982MIT
TDQS
Scored across 31 tools
Most tools map cleanly to a distinct resource-plus-action pair, such as position, orientation, joint target, or simulation state. The only near-overlaps are leer_sensor_proximidad vs comprobar_sensor_proximidad and emparentar_objeto vs crear_union_rigida, but the descriptions explicitly separate those use cases.
The set overwhelmingly follows a Spanish verb_noun pattern using verbs like crear_, fijar_, obtener_, cargar_, and listar_. A few query tools are noun-first instead of verb-first (tiempo_simulacion, estado_simulacion, paso_simulacion), which keeps it from being perfectly uniform.
31 tools is above the 25+ threshold and creates a large selection surface for an agent, even though each tool has a clear niche. The count could be reduced by grouping pose queries, simulation queries, or proximity-sensor variants.
Core workflows are covered well: scene management, object creation and posing, physics setup, joint control, and proximity sensing. Obvious gaps remain for a robotics simulator, such as camera/vision reading, object velocity, collision queries, and script execution, so agents may hit dead ends outside the basic robot-building path.