📝 **[Changelog v1.0.4](./CHANGELOG.md)** - Latest improvements and bug fixes
📝 Changelog v1.0.4 - Latest improvements and bug fixes. That is what the qdrant loader mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
The server publishes 6 tools. What each one is for:
Multi — source connectors**: Git, Confluence (Cloud & Data Center), JIRA (Cloud & Data Center), Public Docs, Local FilesProvider — agnostic LLM**: OpenAI, Azure OpenAI, Ollama, and custom endpoints with unified configurationLogging — Structured logging with configurable levelsCross — document intelligence**: Document similarity, clustering, relationship analysis, and knowledge graphsProduction-ready — HTTP transport with security, session management, and health checksInstallation — The Installation tool exposed by this serverConfiguration is passed through the environment: QDRANT_URL, QDRANT_COLLECTION_NAME, OPENAI_API_KEY, LLM_BASE_URL. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
qdrant-loader on PyPI is all you need. Most clients run it directly, so configuration is a few lines and a restart.
This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Qdrant Loader's toolset — Multi, Provider, Logging and 3 more — is a fair guide to whether it matches your workflow. It is maintained by martin-papy; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| Multi | source connectors**: Git, Confluence (Cloud & Data Center), JIRA (Cloud & Data Center), Public Docs, Local Files |
| Provider | agnostic LLM**: OpenAI, Azure OpenAI, Ollama, and custom endpoints with unified configuration |
| Logging | Structured logging with configurable levels |
| Cross | document intelligence**: Document similarity, clustering, relationship analysis, and knowledge graphs |
| Production-ready | HTTP transport with security, session management, and health checks |
| Installation | The Installation tool exposed by this server. |
{
"mcpServers": {
"qdrant-loader": {
"command": "uvx",
"args": ["qdrant-loader"],
"env": {
"QDRANT_URL": "your-value",
"QDRANT_COLLECTION_NAME": "your-value",
"OPENAI_API_KEY": "your-value",
"LLM_BASE_URL": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| QDRANT_URL | Endpoint or connection string the server talks to. | Yes |
| QDRANT_COLLECTION_NAME | Configuration value read at startup. | Optional |
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| LLM_BASE_URL | Endpoint or connection string the server talks to. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.