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Alternatives to rag-mcp

No user-submitted related servers found.

    Related Servers

    • A
      license
      A
      quality
      B
      maintenance
      Enables AI agents to ingest documentation from websites or local files and perform semantic search over the indexed content using pgvector-backed embeddings.
      5
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables semantic search across text documents using vector embeddings stored in PostgreSQL. Provides multiple search modalities including semantic similarity, question/answer, and style-based search through a retrieval-augmented generation system.
      3
      Apache 2.0
    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables retrieval-augmented generation (RAG) by indexing and searching through documents (Markdown, text, PowerPoint, PDF) using vector embeddings with multilingual-e5-large model and PostgreSQL pgvector. Supports contextual chunk retrieval and incremental indexing for efficient document management.
      71
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables uploading, organizing, and semantically searching documents with support for various file types and embedding providers.
      6 npm
      MIT
    • F
      license
      Not graded
      quality
      D
      maintenance
      Enables RAG (Retrieval-Augmented Generation) capabilities with document processing, vector storage, and intelligent Q\&A using OpenAI embeddings and semantic search.
      -

    TDQS

    A4/5.0

    Scored across 4 tools

    Disambiguation5/5

    Each tool has a distinct purpose: delete_collection removes a collection, ingest_text adds content, list_collections enumerates collections, and search queries them. There is no functional overlap.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun snake_case pattern (e.g., delete_collection, ingest_text, list_collections). Even 'search' fits as a verb describing the action.

    Tool Count5/5

    With 4 tools, the server provides essential RAG operations (CRUD for collections plus search) without being too sparse or overly complex. This is appropriate for its purpose.

    Completeness4/5

    The tool surface covers the core workflow: list collections, ingest text, search, and delete. Missing update or get collection details, but these represent minor gaps that agents can work around.

    Maintenance

    ActivityMaintained
    ResponsivenessResponsive