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BCI-MCP Server

by enkhbold470
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Python
Hybrid

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    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.5/5.0

    Scored across 12 tools

    Disambiguation5/5

    Each tool has a distinct purpose: connection management, data retrieval, recording, neurofeedback control, and event marking. While get_brain_state and stream_summary both provide state snapshots, their descriptions clarify that get_brain_state is detailed and stream_summary is a rolling snapshot, ensuring clear differentiation.

    Naming Consistency5/5

    All tool names follow a consistent pattern: lowercase with underscores, using verbs like 'get_', 'list_', 'start_', 'record', etc. There is no mixing of conventions, making it easy to predict tool names.

    Tool Count5/5

    With 12 tools, the server covers the essential BCI operations—device connection, data streaming, recording, neurofeedback, and calibration—without being overwhelming or sparse. This is a well-scoped set for a BCI server.

    Completeness4/5

    The tool set covers core workflows: connect/disconnect, calibrate, retrieve various brain metrics, record, mark events, and start neurofeedback. However, it lacks explicit stop operations for recording and neurofeedback, which could be a minor gap. Overall, it is largely complete for the stated domain.

    Maintenance

    ActivityMaintained
    ResponsivenessUnresponsive

    Resources