Skip to main content
Glama
AImplifier

eeg-mcp

by AImplifier
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to eeg-mcp

No user-submitted related servers found.

    Related Servers

    • neuro-mcp

      AImplifier
      A
      license
      Not graded
      quality
      C
      maintenance
      An MCP server that gives AI agents a unified interface to clinical/research EEG workflows: signal processing, source imaging, persistent dataset/EHR storage, and web visualization.
      84 PyPI
      1
      BSD 3-Clause

    • BCI-MCP Server

      enkhbold470
      A
      license
      A
      quality
      C
      maintenance
      A framework that integrates Brain-Computer Interface technology with the Model Context Protocol to enable real-time neural signal processing and AI-powered interactions for healthcare, accessibility, and research applications.
      12
      22
      MIT

    • MNE-MCP

      Exekiel179
      A
      license
      B
      quality
      C
      maintenance
      An MCP server that gives AI assistants conversational access to MNE-Python for analyzing neurophysiology data (EEG, MEG, sEEG, ECoG, fNIRS). Enables plain-language analysis pipelines, from loading recordings to generating figures and explanations.
      41
      73 PyPI
      10
      MIT

    • anthropic_skill

      williamchek
      F
      license
      Not graded
      quality
      D
      maintenance
      A robust, lightweight Model Context Protocol (MCP) server designed to empower your AI Agents with context-awareness, safe execution sandboxes, and dedicated thought logs.
      -
    Deploy Server
    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

    A3.7/5.0

    Scored across 47 tools

    Disambiguation5/5

    Every tool targets a distinct resource and action, with nuanced distinctions clearly explained (e.g., get_events vs list_recording_events, start_stream vs start_replay). No two tools appear to do the same thing, even in dense areas like stimulation and processor management.

    Naming Consistency5/5

    Tool names follow a consistent snake_case verb_noun pattern (list_, get_, start_, stop_, attach_, detach_, set_, clear_) with a predictable 'status' suffix for health-check tools (stream_status, processor_status). No mixing of conventions or vague verbs.

    Tool Count2/5

    At 47 tools, the surface is far above the 'heavy' threshold of 25. While the broad scope of a full EEG platform justifies many operations, the sheer number is overwhelming for an agent to navigate efficiently and risks tool-selection errors in practice.

    Completeness5/5

    The tool set covers the entire EEG workflow: acquisition, filtering, analysis, recording, replay, stimulation, monitoring, events, and processors. Lifecycle actions have counterparts (start/stop, attach/detach, arm/disarm), and there are no obvious dead ends or missing core operations.

    Maintenance

    ActivityStale
    ResponsivenessUnresponsive

    Resources