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Alternatives to cheap-eyes

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    Related Servers

    • A
      license
      A
      quality
      A
      maintenance
      Enables coding agents to delegate the reading of large files, folders, and long logs to local models, returning short answers whose every claim is backed by a quote verified word for word in the source with its real file:line. Everything runs on the user's own hardware, so no file content ever leaves the machine.
      3
      MIT
    • A
      license
      A
      quality
      C
      maintenance
      Enables any AI coding agent to search huge codebases cheaply by sending symptom-based locate, classify, and typed decision questions to OpenRouter decision models, which return ranked file:line locations, confidence scores, and the relevant code instead of the whole repository. It supports model selection, context-aware chunking, and fallbacks so agents can pinpoint bugs or filter files for a fraction of a cent.
      4
      2
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables citation-backed question answering over your own notebook sources and programmatic generation of audio overviews, video, infographics, reports, presentations, data tables, flashcards, quizzes, and mind maps, with multi-account rotation and automatic re-authentication. The same engine is also exposed as a 33-endpoint local HTTP REST API for tools such as n8n, Zapier, Make, and curl.
      3,804 npm
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to analyze large codebases and document collections by consulting large context window models (up to 2M tokens) via OpenRouter when files exceed the current agent's context limits.
      358 PyPI
      295
      MIT

    TDQS

    A4.1/5.0

    Scored across 3 tools

    Disambiguation5/5

    Each tool has a clearly distinct role: eyes_stats monitors spend/budget, eyes_run launches a reading job, and eyes_result retrieves or cancels it. The descriptions make the workflow boundaries explicit, so an agent can pick the right one without confusion.

    Naming Consistency5/5

    All three tools follow a strict prefix_noun pattern (eyes_stats, eyes_run, eyes_result) in consistent snake_case. The shared 'eyes_' prefix aids grouping while the suffixes stay distinct.

    Tool Count4/5

    Three tools cover the core lifecycle (check budget, run job, fetch/cancel result) with no redundancy and each earning its place. It is on the lean side, but the scope is narrow enough that this is reasonable rather than thin.

    Completeness4/5

    The surface covers the essential flow: budget check, job execution, result retrieval, and cancellation. A way to list or revisit prior jobs/history is absent, which is a minor gap an agent can mostly work around via the run header id.

    Maintenance

    ActivityNo data
    ResponsivenessSyncing