Enables semantic search and retrieval of documentation using a vector database, allowing users to add and query documentation from URLs or local
Enables semantic search and retrieval of documentation using a vector database, allowing users to add and query documentation from URLs or local files using natural language. That is what the ragdocs mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
A Model Context Protocol (MCP) server that enables semantic search and retrieval of documentation using a vector database (Qdrant). This server allows you to add documentation from URLs or local files and then search through them using natural language queries.
@qpd-v/mcp-server-ragdocs on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
The server publishes 1 tool. What each one is for:
Parameters — - url: URL of the documentation to fetchConfiguration is passed through the environment: QDRANT_URL, EMBEDDING_PROVIDER, OLLAMA_URL, OPENAI_API_KEY, QDRANT_API_KEY. 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.
This sits in the knowledge and memory group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Ragdocs's toolset — Parameters — is a fair guide to whether it matches your workflow. It is maintained by qpd-v; 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We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.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 |
|---|---|
| Parameters | - url: URL of the documentation to fetch |
For OpenAI instead of Ollama:
```json
{
"mcpServers": {
"ragdocs": {
"command": "node",
"args": ["C:/Users/YOUR_USERNAME/AppData/Roaming/npm/node_modules/@qpd-v/mcp-server-ragdocs/build/index.js"],
"env": {
"QDRANT_URL": "http://127.0.0.1:6333",
"EMBEDDING_PROVIDER": "openai",
"OPENAI_API_KEY": "your-openai-api-key"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| QDRANT_URL | Endpoint or connection string the server talks to. | Yes |
| EMBEDDING_PROVIDER | Configuration value read at startup. | Optional |
| OLLAMA_URL | Endpoint or connection string the server talks to. | Yes |
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| QDRANT_API_KEY | Credential the server authenticates with. | Yes |
A knowledge graph your assistant keeps between sessions — entities, relations and observations that persist.
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Your workspace, on speaking terms with AI — search, read and write Notion pages and databases.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Chat with your second brain — search, read and write vault notes through the Local REST API.