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Manage RAGFlow datasets and create retrieval-augmented chat assistants via API.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "RAGFlow MCP Server" yet — see the docs or source repo.
Use the RAGFlow MCP Server to list all datasets in the current account and return their names, IDs, document counts, and last updated times.
A structured list of datasets with key information for each one.
Create a chat assistant based on the specified product documentation dataset, name it "Product Support Assistant," and return the assistant ID plus a summary of available settings.
The assistant is created successfully with its identifier and basic configuration details.
Use the "Product Support Assistant" to answer this question: "What permission management features are supported in the enterprise plan?" Include a summary of the referenced knowledge base content.
A retrieval-grounded answer with a summary of the relevant cited content.
Search and add traceable RAG knowledge for each project workspace.
Use RAG tools for knowledge retrieval, document management, and search visualization.
Turn unstructured documents into a searchable knowledge base for AI agents.
Expose modular retrieval and reasoning tools to AI assistants through MCP.
Retrieve Confluence knowledge with RAG to improve AI-assisted documentation and coding answers.
Connect to a multimodal RAG service for hybrid document search and Q&A.