Skip to main content
Glama
dahx5

sloth-mcp

by dahx5
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to sloth-mcp

No user-submitted related servers found.

    Related Servers

    • Desktop Pilot MCP

      VersoXBT
      A
      license
      Not graded
      quality
      D
      maintenance
      Enables high-speed native macOS automation for Claude by interacting directly with the Accessibility API, AppleScript, and UI trees instead of using screenshots. It allows users to read app states, click elements, and type text semantically across any macOS application.
      7 npm
      11
      MIT

    • mcp-macos-cua

      bradthebeeble
      A
      license
      A
      quality
      D
      maintenance
      Enables AI agents to control macOS desktop apps via screenshots, mouse clicks, keyboard input, accessibility queries, and AppleScript.
      11
      20 npm
      MIT

    • macos-ui-mcp

      thangduonghuu
      A
      license
      A
      quality
      C
      maintenance
      Enables Claude to inspect and drive native macOS app UIs during development via an in-process view tree and screenshot renderer, without requiring screen recording permission.
      7
      MIT

    • Automation MCP

      ashwwwin
      A
      license
      Not graded
      quality
      F
      maintenance
      Enables AI assistants to automate macOS desktop tasks including mouse control, keyboard input, screenshots, window management, and UI interaction.
      8 npm
      414
      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

    A4.3/5.0

    Scored across 12 tools

    Disambiguation4/5

    Most tools are clearly distinct: observe (live screen capture) vs recall (stored memory), focus_app, and execute_plan each have unique roles. The only mild overlaps are observe/recall (both surface interface info) and annotate_icons/probe_tooltips (both label icons), but the descriptions explicitly distinguish these as complementary halves of a workflow, so misselection is unlikely.

    Naming Consistency3/5

    The set mixes conventions: bare verbs (observe, recall), verb_noun (focus_app, execute_plan, annotate_icons, submit_icon_labels, probe_tooltips), and namespace_verb (mission_start, mission_status, mission_finish, mission_replay). The mission_* family is internally consistent and everything remains readable, but no single predictable pattern spans the whole surface.

    Tool Count4/5

    12 tools is well within a reasonable range for a UI-automation agent, and each earns its place (icon-labelling trio, mission lifecycle family, observation/action core). Slightly top-heavy in that five of twelve tools serve the mission subsystem, but that is a justified feature area rather than filler.

    Completeness4/5

    The surface covers the agent's lifecycle well: observe/recall for perception, execute_plan for action, focus_app for context, a full icon-labelling loop, and a complete mission lifecycle (start/status/mark/replay/finish). Minor gaps exist — no explicit abort/stop tool (handled via the corner gesture) and no way to prune stale memory — but nothing that would block core workflows.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources