Docs: Restructured MCP-specific documents (#7565)

### What problem does this PR solve?


### Type of change


- [x] Documentation Update
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{
"label": "MCP",
"position": 4,
"link": {
"type": "generated-index",
"description": "Guides and references on accessing RAGFlow's knowledge bases via MCP."
}
}

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---
sidebar_position: 4
sidebar_position: 1
slug: /launch_mcp_server
---
@ -177,24 +177,6 @@ Run the following to check the logs the RAGFlow server and the MCP server:
docker logs ragflow-server
```
## MCP client example
We provide a *prototype* MCP client example for testing [here](https://github.com/infiniflow/ragflow/blob/main/mcp/client/client.py).
:::danger IMPORTANT
If your MCP server is running in host mode, include your acquired API key in your client's `headers` as shown below:
```python
async with sse_client("http://localhost:9382/sse", headers={"api_key": "YOUR_KEY_HERE"}) as streams:
# Rest of your code...
```
:::
## Tools
The MCP server currently offers a specialized tool to assist users in searching for relevant information powered by RAGFlow DeepDoc technology:
- **retrieve**: Fetches relevant chunks from specified `dataset_ids` and optional `document_ids` using the RAGFlow retrieve interface, based on a given question. Details of all available datasets, namely, `id` and `description`, are provided within the tool description for each individual dataset.
## Security considerations
As MCP technology is still at early stage and no official best practices for authentication or authorization have been established, RAGFlow currently uses [API key](./acquire_ragflow_api_key.md) to validate identity for the operations described earlier. However, in public environments, this makeshift solution could expose your MCP server to potential network attacks. Therefore, when running a local SSE server, it is recommended to bind only to localhost (`127.0.0.1`) rather than to all interfaces (`0.0.0.0`).

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---
sidebar_position: 3
slug: /mcp_client
---
# RAGFlow MCP client example
We provide a *prototype* MCP client example for testing [here](https://github.com/infiniflow/ragflow/blob/main/mcp/client/client.py).
:::danger IMPORTANT
If your MCP server is running in host mode, include your acquired API key in your client's `headers` as shown below:
```python
async with sse_client("http://localhost:9382/sse", headers={"api_key": "YOUR_KEY_HERE"}) as streams:
# Rest of your code...
```
:::

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---
sidebar_position: 2
slug: /mcp_tools
---
# RAGFlow MCP tools
The MCP server currently offers a specialized tool to assist users in searching for relevant information powered by RAGFlow DeepDoc technology:
- **retrieve**: Fetches relevant chunks from specified `dataset_ids` and optional `document_ids` using the RAGFlow retrieve interface, based on a given question. Details of all available datasets, namely, `id` and `description`, are provided within the tool description for each individual dataset.
For more information, see our Python implementation of the [MCP server](https://github.com/infiniflow/ragflow/blob/main/mcp/server/server.py).

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@ -85,7 +85,7 @@ Yes, you can. Just one graph is generated per knowledge base. The smaller graphs
### Does the knowledge graph automatically update when I remove a related file?
Nope. The knowledge graph does *not* automatically update *until* a newly uploaded graph is parsed.
Nope. The knowledge graph does *not* automatically update *until* a newly uploaded document is parsed.
### How to remove a generated knowledge graph?

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- [Set page rank](./guides/dataset/set_page_rank.md)
- [Enable RAPTOR](./guides/dataset/enable_raptor.md)
- [Set variables for your chat assistant](./guides/chat/set_chat_variables.md)
- [RAGFlow MCP server overview](./develop/launch_mcp_server.md)
- [Launch RAGFlow MCP server](./develop/mcp/launch_mcp_server.md)
## v0.17.2