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QDrant Loader MCP Server

@martin-papy

代码仓库主页

基本信息

分类

数据库

许可证

GPL-3.0

传输方式

stdio

作者

martin-papy

关于 QDrant Loader MCP Server

Enterprise-ready vector database toolkit for building searchable knowledge bases from multiple data sources. Supports multi-project management, automatic ingestion from Confluence/JIRA/Git, intelligent file conversion (PDF/Office/images), and semantic search. Includes MCP server

#python#openai#developer-tools#knowledge-base#file-conversion#enterprise-ready#semantic-search#multi-project#cli-tool#document-processing#embbedings#git-integration#rag#jira-integration#cursor-ide#llm-integration#mcp-server#confluence-integration

QDrant Loader MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "qdrant-loader": {
      "command": "uv",
      "args": [
        "sync",
        "--all-packages",
        "--all-extras"
      ]
    }
  }
}

QDrant Loader MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is QDrant Loader?

QDrant Loader is a data ingestion and retrieval system that collects content from multiple sources, processes and vectorizes it, then provides intelligent search capabilities through a Model Context Protocol (MCP) server for AI-powered development workflows.

How to use QDrant Loader?

Install via pip install qdrant-loader qdrant-loader-mcp-server, initialize a workspace with qdrant-loader init, configure data sources in config.yaml and environment variables in .env, ingest data with qdrant-loader ingest, then start the MCP server using the command mcp-qdrant-loader --env /path/to/.env.

Key features of QDrant Loader

  • Multi-source connectors: Git, Confluence, JIRA, Public Docs, Local Files.
  • File conversion: PDF, Office docs, images, audio, EPUB, and more.
  • Smart chunking with hierarchical context and incremental updates.
  • Provider-agnostic LLM support: OpenAI, Azure OpenAI, Ollama, custom endpoints.
  • MCP protocol 2025-06-18 with dual transport (stdio + HTTP).
  • Advanced search tools: semantic, hierarchy-aware, similarity, clustering, knowledge graphs.

Use cases of QDrant Loader

  • AI-powered development with Cursor, Windsurf, and other MCP-compatible tools.
  • Knowledge base creation from technical documentation.
  • Intelligent code assistance with contextual information.
  • Enterprise content integration from multiple data sources.

常见问题

1

What does QDrant Loader integrate with?

QDrant Loader integrates with Qdrant vector database, MCP-compatible tools (Cursor, Windsurf, Claude Desktop), and multiple LLM providers (OpenAI, Azure OpenAI, Ollama, custom endpoints).

2

What are the dependencies and runtime requirements?

QDrant Loader requires Python, a running Qdrant instance, and an LLM provider endpoint. Environment variables for Qdrant URL, collection name, and LLM API key must be configured in a `.env` file.

3

Where does ingested data live?

Data is stored in a Qdrant vector database collection specified by the `QDRANT_COLLECTION_NAME` environment variable. Configuration files (`config.yaml`) define source connections and processing settings.

4

What transports and authentication does the MCP server support?

The MCP server supports stdio and HTTP transport (with SSE for streaming). Authentication is handled via environment variables (e.g., `OPENAI_API_KEY`) and configuration files, with no built-in auth for the HTTP transport mentioned.

5

How do I configure QDrant Loader for my project?

Create a `config.yaml` file with project sources (Git, Confluence, JIRA, etc.) and Qdrant/LLM settings, then define environment variables in a `.env` file for sensitive values. Use `qdrant-loader init --workspace .` to generate templates.

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