Skip to main content
Glama
ModelAtlas

ModelAtlas

by rohanvinaik

Related Servers

Alternatives to ModelAtlas

No user-submitted related servers found.

    Related Servers

    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI coding agents to maintain persistent memory across repositories by indexing local files into a semantic graph and providing ranked, verified search results without LLM token costs.
      3 npm
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables AI assistants to perform semantic, hybrid, and filtered search on indexed local documentation with RAG capabilities.
      2
      MIT
    • A
      license
      B
      quality
      A
      maintenance
      Enables AI coding agents to build and query local repository memory, returning ranked files, context packs, relationship maps, semantic recall, and feedback-tuned search without uploading code.
      9
      158 PyPI
      1
      MIT
    • A
      license
      A
      quality
      D
      maintenance
      Enables AI assistants to perform intelligent semantic code search across codebases using local AI embeddings for meaning-based retrieval.
      6
      26 npm
      MIT

    TDQS

    A4.2/5.0

    Scored across 10 tools

    Disambiguation5/5

    Every tool has a clearly distinct purpose: core recommendation, HF search, detail, comparison, index building, status, generic search, vibe setting, source listing, and enrichment progress. No two tools overlap in function.

    Naming Consistency5/5

    Tools follow a consistent verb_noun pattern with clear prefixes ('hf_' for HuggingFace, 'search_', 'set_', 'list_', 'navigate_'). The naming is predictable and descriptive.

    Tool Count5/5

    With 10 tools, the server is well-scoped. Each tool earns its place, covering discovery, retrieval, comparison, management, and enrichment without unnecessary bloat.

    Completeness5/5

    The tool surface is complete for the server's purpose: navigating models (core recommendation), searching across sources, fetching details, comparing, building indexes, and adding metadata. No obvious gaps.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues