Tencent Yuanqi MCP. Manage Agents, Documents, and Usage Tracking.
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Tencent Yuanqi MCP Server manages specialized AI agents and knowledge bases. Use this server to chat with published assistants, upload documents for RAG context, monitor API usage, or check file processing status directly from your agent framework.
What your AI agents can do
Chat
Sends a message to a specific Tencent Yuanqi assistant ID and receives the conversational response.
Delete file
Removes an uploaded file from your account's knowledge base documents.
Get assistant info
Retrieves detailed metadata and configuration settings for a specified assistant ID.
You can send messages to published agents and track the conversation context using the chat tool.
The server lets you upload files (upload_file), list them (list_files), or delete them (delete_file) from your knowledge base.
Use get_file_status to monitor if a document is finished being indexed and ready for agent use.
Retrieve detailed configuration or basic information about any existing assistant with get_assistant_info.
Check your current token consumption and remaining quota using the get_usage tool, which is critical for billing and planning.
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Tencent Yuanqi MCP Server: 9 Tools for Agent & File Management
Use these nine tools to automate agent interactions, handle document lifecycles, and monitor system performance within your AI client.
019d848bchat
Sends a message to a specific Tencent Yuanqi assistant ID and receives the conversational response.
019d848bdelete file
Removes an uploaded file from your account's knowledge base documents.
019d848bget assistant info
Retrieves detailed metadata and configuration settings for a specified assistant ID.
019d848bget file info
Fetches general metadata, like file name or size, for a specific uploaded document.
019d848bget file status
Checks the current processing status of an uploaded file to see if it's ready for use in RAG.
019d848bget usage
Returns a report detailing your API token consumption and remaining quota limits.
019d848blist assistants
Retrieves an inventory list of all the assistants you have created in your account.
019d848blist files
Outputs a list of all documents currently stored and managed within your knowledge base.
019d848bupload file
Sends a document to the Yuanqi platform so it can be processed and used by agents for context.
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 Tencent Yuanqi, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
This MCP connects your AI client directly to Tencent Yuanqi, giving you nine specific tools to manage specialized agents and handle knowledge documents. You're talking to an intelligent agent platform powered by the Hunyuan model, and these tools let you control everything from conversation context to raw data lifecycle.
To interact with published assistants, you use chat; this sends a message to a specific Tencent Yuanqi assistant ID and immediately returns the conversational response. If you're building complex flows that require tracking history, it handles those persistent context sessions for you.
When you need to manage your knowledge base—the stuff your agents read from—you first use upload_file to send a document to the Yuanqi platform; this gets the file processed and ready for agent consumption. Once uploaded, you can list all stored documents using list_files, which outputs an inventory of everything currently in your knowledge base.
If that document needs to be removed later, you use delete_file to take it out.
Since agents need context before they're useful, you often have to wait for the uploaded files to get indexed. That's where get_file_status comes in; it checks if a document is finished being processed and ready for actual RAG use. You also can check basic file details—like its name or size—by calling get_file_info.
To manage the agents themselves, you first get an overview of what's available by running list_assistants, which retrieves a full list of every assistant you've created in your account. For deeper technical dives, get_assistant_info pulls detailed metadata and configuration settings for any specific assistant ID. This gives you granular control over how those agents function.
Lastly, when you're running these tools frequently, you gotta watch the billing. The get_usage tool returns a clear report showing your API token consumption and what quota limits you still have remaining. You can also check the system status by retrieving detailed metadata about any specific assistant ID using get_assistant_info.
These tools let you control the entire document lifecycle—uploading, listing, deleting, checking status, and getting basic file info—while also letting you chat with agents, list all available agents, and track your usage right from your agent framework. You're running a tight operation here; this server makes sure every step of the way is accounted for.
How Tencent Yuanqi MCP Works
- 1 First, subscribe to the server. Then, log into the Tencent Yuanqi platform and generate an API Key under your 'My Creations' area.
- 2 Next, insert that API Key and your Assistant ID into the client fields provided by Vinkius.
- 3 Your AI client can now use the nine tools—calling them through structured prompts to manage agents or documents.
The bottom line is that this MCP lets you wrap all of Yuanqi's backend operations (file handling, chat, usage tracking) into your existing agent framework.
Who Is Tencent Yuanqi MCP For?
This tool is for the developer who needs to integrate specialized, high-performance Chinese-native AI agents into a larger application. If you're tired of building manual workflows just to check if a document has finished indexing or to manually list every available assistant, this is for you.
You use the server to programmatically automate document ingestion and reliably test agent responses within your custom application.
You maintain dynamic, complex knowledge bases across multiple specialized agents by systematically uploading, listing, and tracking documents.
You integrate high-performance, production-ready AI components into a product while ensuring you can programmatically track usage quotas (get_usage) and manage agent lifecycles.
What Changes When You Connect
- Stop guessing if a document is indexed. Use
get_file_statusto confirm real-time processing status before an agent attempts to read it. - Never lose track of your agents. With
list_assistants, you get a clear inventory, letting you quickly select the right assistant ID for chat or development calls. - Keep costs predictable. The
get_usagetool lets you monitor API token consumption programmatically, so you don't hit an unexpected quota wall mid-run. - Build full knowledge graphs. You can use
upload_fileto ingest source materials and then callchatusing those files as context for deep agent responses. - Clean up old data. Need to remove a document from the system? Use
delete_fileinstead of relying on manual dashboard deletion. - Get full control over your environment. The
get_assistant_infotool lets you pull metadata, ensuring your application knows exactly what parameters an assistant requires.
Real-World Use Cases
The Agent needs context before answering.
A user asks a question that requires company specs. Instead of just calling chat, your agent first calls upload_file with the 'specs.pdf'. Then, it waits for the document to process (checking via get_file_status) and finally executes chat using the newly indexed context. This ensures accurate answers every time.
Auditing a production agent's behavior.
A product team needs to know if they are close to hitting their usage limit. They run an API call that executes get_usage first, checking the quota. This prevents service interruption and allows them to scale up before users even notice a slowdown.
Debugging agent configuration.
A developer is debugging why one specialized bot isn't responding correctly. They use list_assistants to verify the correct ID, then call get_assistant_info for that ID. This confirms all parameters are set up right before they attempt a test chat.
Maintaining data hygiene.
A knowledge manager finds old, irrelevant documents in the system. They use list_files to see everything stored, then systematically call delete_file on every document that shouldn't be indexed anymore.
The Tradeoffs
Assuming file readiness.
An agent tries to run a query or chat immediately after calling upload_file, assuming the data is instantly available for retrieval. This results in vague, incorrect answers because the document hasn't finished indexing.
→
Always check the status first. After calling upload_file with a new document, your agent must call get_file_status. Wait until the server confirms the file is 'processed' or 'ready' before attempting to use it in any chat function.
Ignoring API limits.
Running a large-scale test suite of agent interactions without checking usage. This rapidly consumes tokens, leading to an unexpected quota failure and halting the entire test run midway through.
→
Build checks into your workflow. Before running any batch of calls (like multiple chat calls), start by calling get_usage. This gives you a clear picture of available resources.
Relying on UI for management.
The developer manually goes through the web dashboard to list assistants or check metadata. This is slow, non-repeatable, and impossible to integrate into an automated workflow.
→
Use the tools directly. Instead of clicking buttons, call list_assistants to get a machine-readable ID list, then use get_assistant_info for detailed parameters.
When It Fits, When It Doesn't
Use this MCP Server if your primary need is orchestrating agent workflows that depend on managed knowledge bases and real-time usage tracking. You're building an application where the input data (files) or the agents themselves are complex, specialized components.
Don't use it if:
1. You just need simple chat access without file management; a basic messaging API might suffice.
2. Your files don't require indexing/RAG and you only need to store them in buckets (a general cloud storage SDK is better).
3. All your agents are simple, non-persistent scripts that never change. This server provides the necessary governance for dynamic, multi-agent systems.
If you need to manage the lifecycle of AI assets—from document ingestion (upload_file) to usage auditing (get_usage) and final interaction (chat)—this is your go-to tool.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Tencent Yuanqi. 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.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
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Sandboxed per request
Zero-Trust Proxy
No stored credentials
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Policy on every call
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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 server provides 9 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Managing documents for agents shouldn't require five different tabs and three manual checks.
Today, if you want an agent to answer based on a new report, the workflow is messy. You upload the document in one place; then, you have to switch tabs to check its status—is it indexed? If not, you wait and manually refresh until it says 'ready.' Finally, you copy the ID over to your chat script.
With this MCP server, that whole dance disappears. You call `upload_file` once. Your agent then calls `get_file_status`. It waits programmatically until the status is confirmed. When it's ready, *then* your agent executes `chat`, guaranteeing the response uses fresh, indexed data.
Tencent Yuanqi MCP Server: Control assistant interactions and file access.
Before this server, getting a full picture of an AI agent meant guessing which ID to use or manually looking up its configuration parameters. If you wanted to know if the bot supported external context, you'd be stuck clicking through documentation pages.
Now, you just call `get_assistant_info`. The API returns all the metadata you need—the name, the creator, and the specific capabilities. It makes building reliable, automated agent pipelines straightforward.
Common Questions About Tencent Yuanqi MCP
How do I check if my uploaded file is ready for use with chat? +
You must call get_file_status first. This tool checks the document's processing state, letting you know exactly when it moves from 'uploaded' to 'ready,' which prevents your agent from failing.
What should I use if I need a list of all available assistants? +
Use list_assistants. It gives you an immediate, machine-readable inventory of every assistant ID in your workspace. This is better than manually navigating the UI.
Does the chat tool handle file context automatically? +
No, it doesn't. You must use upload_file and confirm readiness with get_file_status before calling the chat tool to ensure the agent uses your desired documents.
How can I track my token usage programmatically? +
Call get_usage. This returns a direct report on your current API quota and consumption, letting you build limits into your application logic before they hit zero.
How do I check the full configuration of an agent using `get_assistant_info`? +
It retrieves all metadata and setup details for any specific assistant ID. This lets you verify parameters or see if an agent needs updates before calling it.
What command should I use to list every document uploaded using `list_files`? +
list_files returns the IDs and names of every document linked to your account. You can run this tool for an audit of all knowledge bases available.
If a document is obsolete, how do I permanently remove it with `delete_file`? +
The delete_file tool removes the file ID you provide from your knowledge base. Note that this action is irreversible; double-check the file ID first.
Before using a document, how can I inspect its details with `get_file_info`? +
This tool pulls the complete metadata for any given file ID. You get details like size and upload date without having to process or read the content.
Can my AI automatically find the details of an assistant just by providing its ID? +
Yes! Use the get_assistant_info tool with the Assistant ID. Your agent will respond with complete metadata, configuration, and capabilities connected to that specific agent in seconds.
How do I check the remaining token quota for my account? +
Simply ask the agent to run the get_usage action. It will retrieve the current token consumption statistics and the remaining quota provided by the Yuanqi platform.
Can I upload multiple files for my assistant's knowledge base? +
Yes! Use the upload_file tool for each document you want to add. After uploading, use the list_files tool to verify they are processed and available for RAG operations.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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