Augments AI responses with relevant documentation context through vector search.
Augments AI responses with relevant documentation context through vector search. Exposed over MCP by the ragdocs mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.
kill-port on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Everything the assistant can do here goes through one of these:
Tools — 8. add_documentation - Add new documentation directly to the system by providing a URL - Automatically fetches, processes, and indexes theYou will need 6 environment variables: EMBEDDING_PROVIDER, EMBEDDING_MODEL, OPENAI_API_KEY, FALLBACK_PROVIDER, FALLBACK_MODEL, QDRANT_URL. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Plenty of knowledge and memory servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Ragdocs's toolset — Tools — is a fair guide to whether it matches your workflow. It is maintained by rahulretnan; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| Tools | 8. **add_documentation** - Add new documentation directly to the system by providing a URL - Automatically fetches, processes, and indexes the content - Supports various web page formats and extracts relevant content - C |
{
"mcpServers": {
"rag-docs": {
"command": "node",
"args": ["/path/to/your/mcp-ragdocs/build/index.js"],
"env": {
"EMBEDDING_PROVIDER": "ollama", // default
"EMBEDDING_MODEL": "nomic-embed-text", // optional
"OPENAI_API_KEY": "your-api-key-here", // required for fallback
"FALLBACK_PROVIDER": "openai", // recommended for reliability
"FALLBACK_MODEL": "nomic-embed-text", // optional
"QDRANT_URL": "http://localhost:6333"
},
"disabled": false,
"autoApprove": [
"search_documentation",
"list_sources",
"extract_urls",
"remove_documentation",
"list_queue",
"run_queue",
"clear_queue",
"add_documentation",
"add_repository",
"list_repositories",
"update_repository",
"remove_repository",
"watch_repository",
"get_indexing_status"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| EMBEDDING_PROVIDER | Configuration value read at startup. | Optional |
| EMBEDDING_MODEL | Configuration value read at startup. | Optional |
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| FALLBACK_PROVIDER | Configuration value read at startup. | Optional |
| FALLBACK_MODEL | Configuration value read at startup. | Optional |
| QDRANT_URL | Endpoint or connection string the server talks to. | 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.