QDrant RagDocs
About
Integrates Retrieval-Augmented Generation using Qdrant vector database and embeddings to enable semantic search and management of documentation.
Details
- Author
- heltonteixeira
- GitHub stars
- 6
- Downloads
- 590
- Categories
- Search, Knowledge Base, Other, AI, Productivity, Developer Tools, Design, Workplace, File Management, Database, Infrastructure
- Tags
- #integration
Jump to
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
QDrant RagDocsCommand (node, npx, python, etc.)npxArguments-
Argument 1
-y -
Argument 2
@highlight/mcp-server
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
-
Argument 1
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"qdrant ragdocs": {
"env": {},
"args": [
"-y",
"@highlight/mcp-server"
],
"command": "npx"
}
}
}Linux
{
"env": [],
"args": [
"-y",
"@highlight/mcp-server"
],
"command": "npx"
}Macos
{
"env": [],
"args": [
"-y",
"@highlight/mcp-server"
],
"command": "npx"
}Windows
{
"env": [],
"args": [
"/c",
"npx",
"-y",
"@highlight/mcp-server"
],
"command": "cmd"
}Search global news using natural language. Webz.io News Search API returns the most relevant articles and content, with filters for source, country, language, date, sentiment, and category.
Agentic RAG over your own documents on an embedded LanceDB, no database server to run. Hybrid search with reranking, Docling parsing for PDFs and 40+ formats, multimodal retrieval, and answers cited to page numbers and section headings.
A Retrieval-Augmented Generation (RAG) server for document processing, vector storage, and intelligent Q&A, powered by the Model Context Protocol.
A local MCP server implementing Retrieval-Augmented Generation (RAG) with sentence window retrieval and support for multiple file types.
A Python server providing Retrieval-Augmented Generation (RAG) functionality. It indexes various document formats and requires a PostgreSQL database with pgvector.
Production-ready RAG out of the box to search and retrieve data from your own documents.
Vectorize MCP server for advanced retrieval, Private Deep Research, Anything-to-Markdown file extraction and text chunking.
Codicil indexes a repo's Markdown/YAML/TOML docs into a local Chroma store and exposes query_docs/reindex_docs over MCP. Uses Ollama embeddings when available; with zero infra beyond that, it degrades to live keyword search off disk instead of failing.
A server for Retrieval-Augmented Generation (RAG) using the Contextual AI platform.
Creates a personal, always-current knowledge base for AI by indexing documentation from websites, GitHub, npm, PyPI, and local files.
A powerful Model Context Protocol (MCP) server using gemini embedding 3 that transforms any local directory into an ultrafast, visually-aware spatial search engine for AI agents.
Sign in to leave a review
Use Google, GitHub, or an email account so ratings stay tied to real people.
No reviews posted yet.
