Tencent TRTC MCP for AI. Control live video sessions and recordings from your agent.
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Tencent TRTC connects your AI agent directly to a massive real-time video communication engine. It lets you automatically manage live streaming rooms, archive high-definition recordings, and analyze granular call quality metrics from any workflow.
What your AI can do
Describe call detail info
Retrieves detailed metrics about the quality and performance of an active video call session.
Describe room info
Gets current details for a specific TRTC room session, like its status or creation time.
Describe trtc usage
Pulls aggregated statistics showing overall usage patterns and volume of the TRTC service.
Get real-time data points, like frame drops and bandwidth metrics, for immediate call health assessment.
Manage participant lists by querying who is in a session or removing specific users from active rooms.
Start and stop cloud recordings, allowing the agent to archive entire sessions automatically.
Initiate or terminate Multi-Channel Unit (MCU) mixing, which combines multiple video feeds into one output stream.
Retrieve aggregated statistics on how the TRTC service has been used over time for billing or analysis purposes.
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Tencent TRTC: 11 Tools for Real-Time Video Ops
Control complex video conference operations—from managing participants to initiating high-definition cloud archiving—using these eleven specialized tools.
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 Tencent TRTC on VinkiusDescribe Call Detail Info
Retrieves detailed metrics about the quality and performance of an active video call session.
Describe Room Info
Gets current details for a specific TRTC room session, like its status or creation...
Describe Trtc Usage
Pulls aggregated statistics showing overall usage patterns and volume of the TRTC...
Describe User Info
Queries a list of active participants for a known call session using their unique...
Dismiss Room
Forcefully terminates an entire TRTC room session, disconnecting all users...
Remove User
Removes a specific user from a TRTC room given their ID and the room context.
Remove User By Str Room Id
Disconnects one or more users from a room using its string-based ID for targeted removal.
Start Cloud Recording
Initiates the process of recording all video output and activity in an active TRTC...
Start Mcu Mix
Begins multi-channel unit (MCU) mixing, preparing a combined stream from several...
Stop Cloud Recording
Halts an ongoing cloud recording task for a specific TRTC room session.
Stop Mcu Mix
Stops the multi-channel unit mixing process for a given room, freeing up resources.
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Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The Tencent TRTC integration is available immediately — no restart needed.
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Tencent TRTC. 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 11 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Monitoring a live call used to mean clicking through ten different dashboard tabs.
Before this MCP server, checking the health of a multi-person video conference meant having three people—the client, the moderator, and the tech support person—all logging into separate admin consoles. You'd cross-reference user lists, check for 'dropped frame' warnings in one tab, and then manually verify if recording was active in another.
Now, your agent calls `describe_call_detail_info`. It pulls all that data into a single, actionable block. You don't have to correlate metrics across systems; you just get the clean status report.
Tencent TRTC MCP Server: Control video ops from chat
Previously, initiating an archive required a human following a multi-step protocol: log into the system, navigate to 'Recording Management,' select the session ID, and click 'Start Recording.' It was slow and prone to human error.
With this server, your agent just runs `start_cloud_recording` in one command. The entire complex process—authentication, room check, start signal—happens instantly in the background. No clicks needed.
What your AI can actually do with this
You're hooking your AI agent up to a massive real-time video comms engine from Tencent TRTC. This isn't just for basic calls; you use it when y’all need automated control over live streams, detailed metrics on call health, and professional archiving capabilities. It lets your agent manage everything from who's in the room to combining multiple feeds into one single output.
Monitoring Call Performance and Room Status
When you run into a flaky connection or just need an audit trail, you can assess the session metrics using describe_call_detail_info. This tool pulls detailed data points about the live video call's quality and performance—you'll get specifics on things like frame drops and bandwidth usage. For a quick status check, you use describe_room_info to pull current details for any specific TRTC room session, figuring out if it's active or when it was even created.
If your agent needs to know who’s actually on the line, you call describe_user_info, passing in a known call session ID and getting back a list of all unique participant IDs. If you need a high-level overview for billing or resource planning, you pull aggregated service data by invoking describe_trtc_usage to see overall usage patterns and volume statistics.
Controlling User Presence in Rooms
Your agent has full control over who's talking shop. If someone needs to be kicked out—whether they’re spamming or the meeting is done—you can force termination of an entire session using dismiss_room, which immediately disconnects every user in that TRTC room. You don't have to dismiss everyone, though. To target specific people, you use remove_user by providing a user’s ID and the active room context.
If you know the string-based room ID but not individual user IDs, you can manage multiple exits at once calling remove_user_by_str_room_id. These tools let your agent surgically control participant lists in real time.
Managing Video Streams and Archiving Content
If y’all need proof of what happened, or if the content is valuable enough to keep, you manage stream archiving. To start recording every video output and activity happening in an active room, you run start_cloud_recording. When the session wraps up, you use stop_cloud_recording to halt that specific cloud task.
For advanced mixing—say, when three different people are talking but you only want one clean feed for a presentation—you initiate the process with start_mcu_mix, which starts multi-channel unit (MCU) mixing and combines multiple video feeds into a single stream source. When the mix is done, you stop it by calling stop_mcu_mix, freeing up those resources.
How It Works Under the Hood
To get started, your agent needs credentials: Tencent SecretId, SecretKey, and SdkAppId. You're meant to inject these into your LLM logic so it treats this server like an automated stream backend. Once connected, your agent can invoke specific tools—like remove_user or start_cloud_recording—to execute complex video operations you otherwise couldn't touch.
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Here's how it actually works
The bottom line is: you provide the credentials and the desired action; the MCP server handles all the secure connection logic to manage the video stream.
Subscribe to the server and gather your necessary credentials: Tencent SecretId, SecretKey, and SdkAppId.
Inject those three pieces of data into your AI agent's logic so it can connect to the video backend.
The agent calls a function—for example, start_cloud_recording—and the server executes the complex, stateful action.
Who is this actually for?
This is for backend systems that handle high-concurrency, mission-critical real-time media. Think of the cloud DevOps engineer who needs to diagnose why a client's feed keeps dropping frames, or the incident responder who must automatically terminate rogue streaming sessions.
Uses this to analyze network quality after an outage. They run describe_call_detail_info to check for frame drops and diagnose connection issues before calling support.
Needs to maintain order in large virtual rooms. They use tools like remove_user or dismiss_room when a participant violates guidelines.
Integrates this into an automated workflow pipeline, ensuring that every important call session automatically triggers start_cloud_recording for compliance.
What Changes When You Connect
Analyze connection stability. Use describe_call_detail_info to read real-time call performance matrices, instantly pinpointing dropped frames or bandwidth bottlenecks.
Automate compliance archiving. With start_cloud_recording, you can trigger full session recordings without manual intervention, ensuring every important meeting is saved.
Maintain room order. If a user breaks rules, the agent executes remove_user or dismiss_room. These tools handle the cleanup instantly, keeping sessions clean.
Manage complex streams efficiently. Start and stop mixing feeds using start_mcu_mix and stop_mcu_mix, allowing your workflow to dynamically adjust stream complexity.
Audit usage automatically. The describe_trtc_usage tool gives you a historical view of service utilization, which is critical for cost tracking or capacity planning.
See it in action
The Compliance Archive Trigger
A training session concludes. Instead of an employee having to manually hit 'record,' the agent detects the room leaving a certain state and immediately calls start_cloud_recording. This ensures zero-effort, automated archiving for legal compliance.
The Lag Diagnosis
A client reports their video is choppy. The ops engineer asks the agent to run diagnostics. The agent uses describe_call_detail_info and immediately gets frame drop analytics, proving if the issue is local or network-wide.
The Rogue User Cleanup
A moderator detects a user spamming the chat in a live broadcast. The agent doesn't wait for a human command; it uses remove_user_by_str_room_id to instantly disconnect the offending party, keeping the stream clean.
The Resource Cleanup Flow
A multi-party collaboration meeting is finished. The agent executes a cleanup sequence: first stop_mcu_mix, then checks if recordings are active using describe_room_info, and finally calls stop_cloud_recording to free up storage.
The honest tradeoffs
Assuming the recording started
The agent blindly calls stop_cloud_recording after a meeting, even if no recording was actually initiated or if the previous attempt failed. This just wastes an API call.
Always check the room status first. Use describe_room_info to confirm active tasks before calling stop_cloud_recording. Never assume state changes.
Ignoring user presence
The agent tries to remove a user who has already been dismissed from the room by another process. The tool call fails without context, leaving the workflow hanging.
Before calling remove_user, query the participant list using describe_user_info to validate that the target user ID is actually present and active in the session.
Forgetting resource cleanup
The agent starts an MCU mix or a cloud recording but forgets to write the corresponding stop command. This leaves orphaned, consuming resources running indefinitely.
Treat these tools as paired operations. If you call start_mcu_mix, your workflow must include a cleanup step calling stop_mcu_mix when finished.
When It Fits, When It Doesn't
Use this server if your core process involves high-concurrency, multi-party video streams that require state management. If you need to automate actions like archiving meetings, diagnosing network latency (using describe_call_detail_info), or managing participants in a live room, this is for you. Don't use it if your data needs are simpler: for basic chat logging, simple text message routing, or standard CRUD operations on non-streaming records. For those tasks, stick to general messaging or database tools. The complexity of managing video state (like starting and stopping MCU mixes) means you need a server designed specifically for real-time media ops.
Questions you might have
How do I stop a recording using start_cloud_recording? +
You use stop_cloud_recording. This tool requires knowing the active room session to halt the task. Always check the status first with describe_room_info.
Can I kick a user from a call using remove_user? +
Yes, that's what it does. The agent calls remove_user, and the connection is severed immediately for the specified participant in that room.
What if I need to analyze past usage data? Do I use describe_trtc_usage? +
Yes, run describe_trtc_usage. It pulls aggregated statistics. This is different from real-time metrics; this is for billing or high-level capacity planning.
Do I need to call describe_user_info before removing a user? +
It's safer to use describe_user_info first. This confirms that the unique CommId you intend to remove is currently active and attached to the room.
How do I check the overall status of a session using describe_room_info? +
The tool pulls high-level details about the room. You get things like its unique ID, creation time, and whether it is currently active or suspended.
What specific metrics does describe_call_detail_info provide for quality assessment? +
It delivers granular stream health data. This includes precise measurements of packet loss rates, latency levels, and frame drop analytics in real time.
How do I manage video transcoding and mixing using start_mcu_mix? +
You use this tool to process multiple incoming streams into a single output mix. Remember to call stop_mcu_mix when you are finished with the session.
If I need to immediately end all activity in a room, what does dismiss_room do? +
It terminates the entire TRTC session instantly via its ID. This action removes every connected user and halts all associated streaming tasks for that room.
Does this support Tencent Cloud API v3 signature? +
Yes! The MCP abstraction layer perfectly encapsulates and executes the TC3-HMAC-SHA256 signature algorithm natively so you don't have to code it. Just provide SecretId and SecretKey safely.
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