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embodied-mcp

by DDDB479
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    Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

    TDQS

    B3.4/5.0

    Scored across 13 tools

    Disambiguation4/5

    Most tools target clearly different dataset lifecycle stages: detection, summarisation, validation, quality analysis, segmentation, labelling, conversion, cataloging, and planning. Minor possible confusion exists between detect_dataset and list_formats, and between summarize_dataset/validate_dataset/quality_report, but the descriptions clarify specific versus aggregate and structural versus statistical roles.

    Naming Consistency4/5

    Most names use a readable snake_case convention, with verb_noun patterns like detect_dataset, validate_dataset, convert_dataset, segment_episodes, and plan_windows. The catalog_index/search/stats group is internally consistent, though quality_report and labeling_tasks deviate from the dominant verb_noun style.

    Tool Count5/5

    Thirteen tools is well within the appropriate range and each appears to cover a distinct useful operation in an embodied-AI dataset workflow. The set is broad without feeling bloated, spanning inspection, validation, conversion, cataloging, and project scaffolding.

    Completeness4/5

    The surface covers a wide lifecycle: format detection, schema summary, validation, quality reporting, segmentation, annotation planning, conversion, scaffolding, cataloging, format listing, and training-window planning. Minor gaps remain, such as no explicit catalog deletion/pruning and no tool to materialise planned windows into actual training samples.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

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