ragflow-mcp-server-fixed
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| RAGFLOW_API_KEY | Yes | RAGFlow API key. | |
| RAGFLOW_BASE_URL | Yes | RAGFlow base URL. | |
| RAGFLOW_DEFAULT_CHAT_ID | No | Existing RAGFlow chat assistant ID for ask_configured_chat. | |
| RAGFLOW_DEFAULT_CHAT_NAME | No | Existing RAGFlow chat assistant name for ask_configured_chat. | |
| RAGFLOW_DEFAULT_SESSION_NAME | No | Session name created for the configured chat. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_datasetsB | List RAGFlow datasets. Returns dataset id and name. |
| create_chatB | Create a RAGFlow chat assistant and session for one dataset. |
| chatC | Ask a question in a created RAGFlow chat session. |
| ask_configured_chatA | Preferred one-step QA tool. Ask a question in the RAGFlow chat assistant configured on server startup. No dataset_id, chat_id, or session_id is required. |
| retrieveB | Retrieve relevant chunks directly from a RAGFlow dataset. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Most tools have distinct purposes: listing datasets, retrieving chunks, and creating chat sessions are clear. However, ask_configured_chat and chat both serve QA functions, which could lead to confusion if the agent does not carefully read the descriptions.
Tool names are inconsistent: some are verb_noun (list_datasets, create_chat), others are single verbs (chat, retrieve), and one includes an adjective (ask_configured_chat). This mixed pattern can confuse an agent.
With 5 tools, the surface is slightly lean but still covers essential RAGFlow operations: listing datasets, creating chats, retrieving chunks, and QA. It does not feel bloated, though a few more tools could enhance completeness.
The tool set lacks update and delete operations for both chats and datasets, and there is no tool to manage documents within datasets. This leaves significant gaps for a production RAG system, potentially causing agent failures.