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201,013 tools. Last updated 2026-06-13 23:00

"Hybrid RAG System for Indexing DevOps eBooks and Online Documentation" matching MCP tools:

Matching MCP Servers

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    A modular Retrieval-Augmented Generation (RAG) framework that provides hybrid search and knowledge retrieval capabilities via the Model Context Protocol. It enables users to integrate document-based knowledge into LLM workflows with support for dense/sparse retrieval, reranking, and observability.
    Last updated
    MIT
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    Enables Claude to perform hybrid search across local documents by combining semantic vector retrieval and BM25 keyword matching for optimal context recovery. It supports multiple file formats including PDF, CSV, and Markdown, leveraging local Ollama models for private and efficient document querying.
    Last updated
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    MIT

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  • The MCP server for Azure DevOps, bringing the power of Azure DevOps directly to your agents.

  • Retrieve DevOps documentation templates, code standards, and best practices from GitHub to generate documentation and apply development standards.
    MIT
  • Search indexed code and documentation using hybrid semantic and keyword retrieval. Ideal for answering questions about your codebase and project architecture.
    Apache 2.0
  • Discover available DevOps skills including SAP Fiori documentation templates, code standards, and best practices to prepare for generating documentation or reading project content.
    MIT
  • Check if an EMS system is online and responsive by verifying its operational status with a system ID.
    MIT
  • Upload files to process and index them for searchable knowledge retrieval using RAG (Retrieval-Augmented Generation) technology.
    MIT
  • Export your skills to Weaviate vector database format, enabling hybrid vector and BM25 keyword search for production RAG applications.
    MIT
  • Retrieve a complete list of all files stored in the RAG system for document management and semantic search operations.
    MIT
  • Delete files from the RAG system to manage storage and maintain relevant content for retrieval-augmented generation tasks.
    MIT
  • Add files to a RAG system for document retrieval, supporting PDF, DOCX, TXT, MD, CSV, and JSON formats to enable semantic search and information access.
    MIT
  • Remove stored files, vector memory, or all data from the RAG system to free storage space or reset the knowledge base.
    MIT
  • Find relevant documents in the RAG system using semantic search with customizable similarity thresholds and result limits.
    MIT