RAGFlow MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| api-key | Yes | API key for RAGFlow | |
| base-url | Yes | Base URL of the RAGFlow server |
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 | 列出 RAGFlow 中的所有数据集 |
| create_chatB | 创建一个新的聊天助手，基于指定的数据集 |
| chatC | 向聊天助手提问 |
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 3 tools
Each tool has a clearly distinct purpose: chat queries an assistant, create_chat creates a new assistant, and list_datasets retrieves available datasets. No overlap in functionality.
All tool names follow a consistent snake_case verb_noun pattern (chat, create_chat, list_datasets), making them predictable and easy to understand.
With only 3 tools, the server is minimal. While this might suffice for basic interactions, it feels thin for a full RAGFlow integration.
The tool set covers only basic operations: chatting, creating a chat assistant, and listing datasets. Missing crucial CRUD operations for datasets and assistants, leaving significant gaps for agents.