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so101-mcp

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    • A
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      Enables LLM-based AI agents to control SO-ARM100 and SO-101 robots through natural language commands and camera feedback. It supports various transport protocols and provides tools for both autonomous robotic movement and manual keyboard operation.
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    • A
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    • F
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    TDQS

    A3.7/5.0

    Scored across 8 tools

    Disambiguation4/5

    Each tool maps to a distinct action on a distinct target: read state, list named poses, move to a named pose, absolute multi-joint moves, relative single-joint moves, and three gripper operations. The only mild overlap is move_joints vs. move_joint_by, but the descriptions clearly separate absolute/multi-joint from relative/single-joint semantics, and cycle_gripper is distinguished from open/close by its repeat count.

    Naming Consistency5/5

    All names follow a consistent snake_case verb_noun pattern (get_*, list_*, move_*, cycle_*, open_*, close_*) with predictable, domain-specific nouns. No mixed conventions or vague verbs anywhere in the set.

    Tool Count5/5

    Eight tools is a well-scoped surface for a robot arm: state read, pose listing, three movement primitives, and three gripper operations. Nothing feels redundant or padded, and no important primitive is obviously duplicated.

    Completeness4/5

    The surface covers perception (get_robot_state, list_safe_poses), motion (move_to_pose, move_joints, move_joint_by), and end-effector control (open/close/cycle_gripper) with clear lifecycle coverage. Minor gaps exist: no explicit stop/halt or return-to-home primitive and no way to teach/register a new safe pose, though the latter appears intentionally done by an operator outside the toolset.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues