A
licenseNot graded
qualityD
maintenanceBridges AI agents with Webots robotics simulation, enabling natural language control of Nao robots and visual perception.
MIT
embodied-mcp
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TDQS
A4.3/5.0
Scored across 8 tools
Disambiguation5/5
Each tool serves a distinct function: perception (get_view), grounding (ground), navigation (drive_to, stop), numerical parsing (parse_numerical), counting (count_in_view), finalizing counts (commit_count), and object publication (publish_object). There is no overlap or ambiguous boundaries between them.
Naming Consistency5/5
All tool names follow a consistent verb_phrase pattern with snake_case (e.g., get_view, drive_to, commit_count). The verbs are imperative and descriptive, making the intent of each tool predictable from its name alone.
Tool Count5/5
With 8 tools, the server is well-scoped for embodied navigation and counting tasks. Each tool covers a necessary step in the workflow without redundancy or excessive granularity, fitting within the ideal 3-15 tool range.
Completeness5/5
The tool surface fully covers the lifecycle of the domain: perceiving the environment, grounding references, moving, stopping, parsing questions, counting from multiple views, committing aggregated results, and publishing object markers. There are no obvious gaps or dead ends for the intended tasks.
Maintenance
ActivityMaintained
ResponsivenessNo issues