Skip to main content
Glama
Kvxw1105

lean-computer-use-mcp

by Kvxw1105
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to lean-computer-use-mcp

No user-submitted related servers found.

    Related Servers

    • oswright

      Ask-812
      A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to automate real desktop applications across Windows, Linux, and macOS using incremental screen perception, accessibility trees, OCR, and window management, dramatically reducing token usage compared to screenshot-per-step approaches.
      151 PyPI
      3
      MIT

    • screen-use

      tongriyaotxt
      A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to see, locate UI elements, and operate any Windows desktop app through natural language, using accessibility-tree matching with optional vision-model fallback, plus an autonomous visual loop with introspection and meta-learning.
      1
      MIT

    • AI Process Manager

      aipm-engine
      A
      license
      A
      quality
      A
      maintenance
      Enables AI agents to query Windows process, window, and console information via structured JSON instead of screenshots, reducing token usage by 94-98%.
      20
      21 npm
      1
      MIT

    • Extra

      AIYantra
      A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI assistants to autonomously control Windows 11 and 10 desktops via sub-10ms screen capture, native UI Automation element inspection, and zero-lag keyboard and mouse input. Combines a visual plane with a semantic plane and stall detection so agents can operate real applications reliably without vision-only guessing.
      4
      MIT

    • VoltageInputMcp

      casualkre
      A
      license
      A
      quality
      B
      maintenance
      Enables a frontier model to drive a desktop at input speed by delegating rapid sequences of keyboard and mouse actions to small local models running via a state-machine playbook, without per-action round-trips.
      16
      MIT
    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

    A3.8/5.0

    Scored across 5 tools

    Disambiguation5/5

    Each tool has a distinct role: finding apps, observing state, executing single actions, batch actions, and metrics. No functional overlap between tools, and the descriptions clearly differentiate them.

    Naming Consistency4/5

    All tools share the 'cu_' prefix and are short, but the second part mixes verbs (find, observe, act, batch) with a noun (metrics). This is a minor deviation from a consistent verb-focused pattern.

    Tool Count5/5

    With only 5 tools, the server is lean and well-scoped. Each tool handles a core capability (discover, observe, act, batch, monitor), and the count feels right for the stated purpose.

    Completeness5/5

    The tools cover the essential lifecycle for computer use: finding an app, observing its state, performing actions (single or batched), and tracking metrics. There are no obvious dead ends or missing core operations.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

    Resources