Douyu MCP for AI. Analyze real-time streaming viewership and chat data.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Douyu provides deep API access to live streaming data, letting your agent monitor viewership metrics, process massive bullet-chat streams, and extract audience retention numbers from any client.
It handles everything from identifying streamers to tracking top donors.
What your AI can do
Follow streamer
Automatically tracks a broadcaster’s activity using an OAuth token.
Get danmu config
Retrieves the necessary parameters to connect your agent to the live chat feed (Danmu).
Get gift list
Fetches a list of all active virtual gifts currently available in a room.
Get the current viewer population and detailed stats for a specific live room.
Download high-speed chat data (bullet-chats) to analyze interactions passing through major broadcast environments.
Search for specific broadcasters by name or find all available game and IRL streaming categories.
Pull weekly or monthly leaderboards to see who the top tippers are in a room.
Programmatically send a message into a live stream's comment feed.
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Douyu: 10 Tools for Streaming Data
Use these ten tools in any MCP-compatible client to access specific data points about live streaming activity, chat, viewers, and donors.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Douyu on VinkiusFollow Streamer
Automatically tracks a broadcaster’s activity using an OAuth token.
Get Danmu Config
Retrieves the necessary parameters to connect your agent to the live chat feed...
Get Gift List
Fetches a list of all active virtual gifts currently available in a room.
Get Live Rooms
Gets a current list of streaming rooms running within a specific category.
Get Room Detail
Retrieves detailed metrics and information about one specific live stream room.
Get Stream Categories
Pulls a full list of available game titles and general interest categories.
Get Top Donators
Gets the current leaderboard showing the top tippers for a given time period (weekly/monthly).
Get User Stats
Looks up profile information and basic metrics for any user account.
Search Streamers
Finds streamers by searching using their name or alias.
Send Message
Sends a specific message into the live stream's comment chat feed.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Douyu, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Douyu. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 10 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Getting Live Data From Stream Dashboards Is a Pain Point.
Today, getting real-time metrics means opening four different browser tabs: one for the viewer count, one for the donation history, another just to check what categories are active, and a fourth dedicated chat window. You copy data from the first tab into a spreadsheet, then manually cross-reference the top donors from the second tab with the user stats from the third. It's slow, it’s brittle, and you always end up missing something.
With this MCP, your agent handles that whole sequence automatically. Tell your client to 'give me the current viewer count for all LoL streams.' The result comes back structured—no tabs, no copy-pasting, just clean data ready for analysis.
The `get_danmu_config` Tool Gives You Access to the Chat Feed.
Manual chat monitoring involves watching a constantly scrolling feed and trying to catch every relevant keyword or spam message. If you need to track specific phrases, you’d have to manually scroll back hundreds of messages just to confirm a pattern existed hours ago.
The `get_danmu_config` tool gives your agent the parameters it needs to connect directly to the live chat feed. This means your client can process massive volumes of messages in the background and filter them instantly, giving you immediate insight into community sentiment.
What your AI can actually do with this
You're trying to make sense of the sheer volume of activity happening in major online broadcast hubs—the kind that generate more data than a small city. You don't want to spend hours clicking between dashboard tabs or writing messy scripts just to get a clean view of what’s happening live.
This MCP connects your AI client directly to Douyu's core broadcasting metrics. It lets you ask for specific, real-time details—like how many people are watching a certain room right now, or exactly what kind of gifts users are sending out.
Your agent handles the heavy lifting. You tell it to look up all active streams in a game category, and it pulls that list back instantly. Want to analyze chat? It can download high-velocity comment feeds for you, so your agent can filter spam or track specific keywords across millions of messages.
The whole thing runs through Vinkius, letting you connect this data source alongside hundreds of others, keeping everything in one place.
019d8431-2db9-72ab-ac44-e9154e0b4140 Here's how it actually works
The bottom line is: your AI client uses standard conversation prompts to execute complex, multi-step queries against Douyu’s deep data streams.
Sign up on the official Douyu Open Platform and configure your application with the required security keys.
Connect your AI client to Vinkius, then prompt your agent using natural language requests detailing the streaming data you need.
The MCP executes the request, gathering live metrics—like viewer counts or chat logs—and returns a structured output for your agent to use.
Who is this actually for?
This MCP is for the esports data analyst who can't rely on manual dashboard scraping. It's also perfect for community mod teams needing automated chat monitoring, or sponsorship liaisons who need instant proof of audience engagement.
Pulls historical log metadata and tournament retention stats into CSV format to prove league viability across multiple days.
Monitors chat velocity for spam clusters or toxic behavior, preventing human overhead from managing large Discord-linked channels.
Quickly verifies audience legitimacy by fetching raw engagement metrics to guarantee investment value during brand interactions.
What Changes When You Connect
Monitor audience retention instantly. You can check the current viewer count for a room using get_room_detail or monitor multiple concurrent streams with get_live_rooms, eliminating manual dashboard checking.
Automate deep chat analysis. Instead of reading thousands of comments, your agent uses get_danmu_config to process high-velocity comment streams and filter out spam or track keywords.
Track financial engagement easily. Use get_top_donators to instantly generate leaderboards, which is a massive time save compared to compiling raw donation logs yourself.
Find what you're looking for fast. If you need a specific streamer, use search_streamers. If you want to know all the games being played right now, just call get_stream_categories.
Control the narrative. Need to send a message or announce something? Use send_message to push text directly into the live chat feed.
See it in action
Verifying Sponsorship Value
A liaison needs to prove engagement for an ad buy. They ask their agent to use get_top_donators and then get_user_stats on the top five accounts, getting a raw report of who tipped and how much. No spreadsheets needed.
Monitoring Niche Content
A mod team notices a new genre emerging. They first run get_stream_categories, filter for 'indie games,' then use get_live_rooms to see which streams fit, saving hours of manual browsing.
Debugging Live Chat Flow
A developer needs the chat feed parameters. They run get_danmu_config, get the required WebSocket details, and plug it into their local IDE to test message handling before deploying anything.
Mapping Broadcaster Reach
An analyst wants to see which streamers are active in a particular region or niche. They start by using search_streamers for key aliases, and then run get_user_stats on the results list.
The honest tradeoffs
Using basic web scraping
A user tries to copy-paste data from a stream dashboard or uses simple scrapers, which break whenever Douyu updates its page structure.
The right way is to use the MCP tools. For instance, instead of scraping viewer counts, call get_room_detail directly via your agent for reliable metrics.
Over-relying on one tool
A user only uses get_live_rooms and misses that the chat logs are useless without knowing the connection details.
Remember to pair stream listing with get_danmu_config. You need both data points—the room list and the chat parameters—to get a full picture.
Searching for users manually
Trying to find a streamer's alias and then separately looking up their profile metrics on another site.
Use search_streamers first, then feed the resulting ID into get_user_stats. It keeps everything in one workflow.
When It Fits, When It Doesn't
You should use this MCP if your goal is to extract structured, reliable data—like viewer counts, top donor names, or filtered chat logs—from Douyu's ecosystem. If you need historical log metadata for CSV reporting (e.g., tournament stats), the tools support that via get_top_donators. However, don't use this if your goal is just to watch the stream or analyze subjective content; it only handles structured metrics and chat data. If all you need is a simple visual check of who's online, maybe a manual dashboard glance works, but for anything that needs to be processed by an agent—like filtering spam after calling get_danmu_config—this MCP is non-negotiable.
Questions you might have
How do I check who is watching a specific room using `get_room_detail`? +
The get_room_detail tool returns comprehensive data on the stream, including current viewer population metrics. This lets your agent give you an immediate snapshot of audience retention and size.
Does `search_streamers` only find streamers by name? +
No, it searches for streamers using both their name or their alias. It’s a flexible search that helps your agent locate the correct broadcaster ID quickly.
What is the best way to track top spenders? Should I use `get_top_donators`? +
Yes, using get_top_donators pulls a structured leaderboard of tippers. It’s much better than trying to scrape donation messages and gives you reliable, categorized data.
Can I send a message into the chat using the `send_message` tool? +
Yes, send_message allows your agent to programmatically push text directly into a stream's Danmu chat. This is useful for automated moderation or alerts.
How do I get the necessary parameters to connect my agent to live chat using `get_danmu_config`? +
It retrieves the WebSocket details needed for connection. This gives your AI client the full configuration required to listen to real-time Danmu chat streams automatically, bypassing manual setup in your code.
What does `get_stream_categories` provide so I can filter content types? +
It returns a comprehensive list of all available game and IRL categories. This lets your agent filter streams by specific genres, ensuring you only analyze relevant content types (e.g., 'LoL' or 'Gaming').
If I use `follow_streamer`, do I need to worry about authentication tokens or rate limits? +
The tool requires an OAuth token for continuous following. You must manage this token in your client setup. While the MCP handles basic retries, remember that high-volume polling might hit API usage limits.
What kind of data can I get about virtual items using `get_gift_list`? +
It provides a list of all active virtual gifts available in a room. This is useful for analyzing the community's economic activity and understanding what types of donations or support are popular right now.
Can my AI automatically aggregate bullet-chat density from a major active esports broadcast directly? +
Yes! Utilize the get_room_danmu integration command. Your agent will dynamically hook to the channel pinging the rapid incoming stream mapping it natively to your terminal workspace seamlessly bypassing clunky UI obstacles totally and continuously.
How do I easily discover what tier of streamer category dominates the current local timezone traffic? +
Direct your agent to use the search_category_metrics tool checking the highest weighted nodes inside the response hierarchy sorting viewership metrics heavily bypassing raw unstructured noise dynamically avoiding manual page scrolling routines completely.
Are there any destructive capabilities regarding overarching account deletions or unapproved streaming executions? +
No. The core structure isolates tasks strictly on retrieving passive data arrays without injecting changes downstream blocking inherently all write pathways firmly bypassing structural mutations altogether in the process loop constantly running below.
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