A
licenseNot graded
qualityD
maintenanceEnables semantic retrieval and knowledge base management through the RAGFlow API, including dataset, document, chunk, chat, and graph operations.
7
MIT
ragflow-kb-mcp
by feci
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TDQS
C2.9/5.0
Scored across 92 tools
Disambiguation2/5
The set contains many closely related operations whose boundaries are subtle: ragflow_chat, ragflow_openai_chat_completion, ragflow_search_app_completion, and ragflow_agent_chat all answer questions but differ by backend/session semantics. There are also near-overlaps between upload_file/upload_document/upload_attachment/upload_agent_file, update_chat/replace_chat_settings, delete_chat/delete_chats, and file-manager vs dataset document tools. Descriptions help, but with 92 tools an agent is likely to misselect.
Naming Consistency4/5
Almost all tools use the ragflow_ prefix with a verb_noun pattern (list_datasets, create_chat, delete_documents, update_memory), which is predictable. Minor inconsistencies exist in singular/plural variants (delete_chat vs delete_chats, create_chat_session vs delete_chat_sessions) and in the exceptional replace_chat_settings verb.
Tool Count1/5
With 92 tools, the surface is far beyond a manageable MCP server scope and creates selection overload. Even though the underlying RAGFlow product is broad, this tool count is an extreme mismatch for practical agent use.
Completeness4/5
Coverage is extensive across datasets, documents, chunks, chat assistants, agents, memories, search apps, files, commits, metadata, retrieval, and health checks. Most lifecycle operations are present, with only minor gaps such as no obvious single-document metadata get tool separate from list/download.
Maintenance
ActivityMaintained
ResponsivenessNo recent community issues