Skip to main content
Glama
kaypon

MCP Tooling Lab

by kaypon
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Related Servers

Alternatives to MCP Tooling Lab

No user-submitted related servers found.

    Related Servers

    • ChromaDB MCP Server

      rkilchmn
      A
      license
      Not graded
      quality
      D
      maintenance
      An MCP server that exposes ChromaDB vector database operations, enabling AI assistants to perform collection management and semantic document searches. It supports HTTP, persistent, and in-memory connection modes along with various embedding providers including OpenAI and HuggingFace.
      MIT

    • MCP RAG

      santis84
      A
      license
      B
      quality
      D
      maintenance
      A complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.
      11
      3 npm
      1
      MIT

    • minirag

      mck-s
      A
      license
      Not graded
      quality
      C
      maintenance
      MCP server for a self-hosted RAG system that enables AI tools to search and retrieve grounded answers from locally ingested documents via MCP tools, with local embeddings and no API key required.
      MIT

    • ragi

      SusuTawar
      A
      license
      A
      quality
      D
      maintenance
      Local-first RAG indexing and semantic search MCP server. Enables document retrieval and context-aware queries using local embedding models.
      3
      6 npm
      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

    B3.4/5.0

    Scored across 3 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: embed_text handles raw embedding generation, index_documents combines embedding with indexing, and vector_search performs semantic search. The descriptions clearly differentiate their roles in the embedding/indexing/search pipeline.

    Naming Consistency5/5

    All three tools follow a consistent verb_noun pattern with snake_case: embed_text, index_documents, and vector_search. The naming is predictable and follows the same convention throughout.

    Tool Count3/5

    With only 3 tools, the count feels thin for a 'Tooling Lab' server, which might imply broader capabilities. However, for a focused embedding/indexing/search domain, the minimal set is functional but could benefit from additional utilities like document management or configuration tools.

    Completeness4/5

    The tools cover the core embedding-to-search pipeline well: create embeddings, index them, and search. Minor gaps include lack of document deletion/update operations and no direct embedding storage management, but agents can work around these with the existing tools.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources