Lingyi Wanwu MCP. Orchestrate chat, embeddings, and usage metrics for Yi models.
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Lingyi Wanwu connects your AI agent directly to the Yi LLM ecosystem. This MCP handles chat completions, generates semantic embeddings for RAG pipelines, and provides real-time account usage monitoring.
You get a single point of control over high-performance bilingual models like Yi-Large.
What your AI agents can do
Chat completions
Sends a prompt message to one of the Yi models and returns the generated response.
Check moderation
Runs content through policy filters, flagging any text that violates usage guidelines.
Get embeddings
Takes input text and generates a numerical vector representing its semantic meaning.
Send prompts to Yi models (like chat-34B or Yi-Large) and receive structured text outputs, maintaining context across turns.
Take any piece of text and generate a high-dimensional embedding vector for use in search indexes and RAG systems.
Pass outgoing prompts or generated responses through the moderation tool to check for policy violations before they are sent.
List all Yi model versions and retrieve their specific technical details, helping you choose the right model for the job.
Retrieve current account statistics, including consumed tokens and remaining balance, keeping your operational costs clear.
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Lingyi Wanwu: 5 Tools for Model Operations
Use these tools to manage the entire lifecycle of your LLM integration—from model selection and conversation running to cost tracking.
019d8453chat completions
Sends a prompt message to one of the Yi models and returns the generated response.
019d8453check moderation
Runs content through policy filters, flagging any text that violates usage guidelines.
019d8453get embeddings
Takes input text and generates a numerical vector representing its semantic meaning.
019d8453get usage
Retrieves current account metrics, showing token consumption and remaining balance.
019d8453list models
Fetches a list of all accessible Yi model names and their technical specifications.
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 Lingyi Wanwu, 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
Lingyi Wanwu connects your AI agent right into the whole Yi LLM ecosystem. You're getting a single point of control over high-performance, bilingual models like Yi-Large. This MCP handles everything you need—from running chats to generating vectors and keeping tabs on what you spend.
You can use the chat_completions tool to send any prompt message to one of the available Yi models; it'll return a generated response while maintaining context across multiple turns in the conversation. Before sending or receiving text, you can pass content through check_moderation. This tool runs your prompts and responses against policy filters, flagging anything that violates usage guidelines so you know your output is clean.
When you need to power up an advanced search index or build out a Retrieval Augmented Generation (RAG) system, use get_embeddings. It takes any piece of text you throw at it and generates a high-dimensional numerical vector representing the semantic meaning. For model selection, the list_models tool lets you fetch a complete list of all accessible Yi models, giving you their specific technical specs so you know exactly what you're working with.
Lastly, tracking costs is simple. The get_usage tool retrieves your current account metrics. It shows you how many tokens you've consumed and what your remaining balance is. You can keep an eye on your operational spending without having to check a dashboard manually.
How Lingyi Wanwu MCP Works
- 1 Subscribe to the server. Then log into the Lingyi Wanwu Developer Platform.
- 2 Generate a new API Key within the platform's 'API Keys' section.
- 3 Insert your unique API Key into the field provided here. Your agent can now manage Yi model workflows.
The bottom line is: you get a single, authenticated connection point to run complex LLM logic without worrying about underlying credentials or setup steps.
Who Is Lingyi Wanwu MCP For?
This is for the ML Engineer who needs predictable model access. It's also for the Data Architect building RAG pipelines that need reliable embeddings, and the Backend Developer responsible for monitoring API costs in production. If you deal with high-volume LLM calls, this saves you time.
Needs to swap between models (e.g., chat-34B for general chat vs. specialized model) and needs programmatic access to embeddings.
Builds RAG pipelines that require consistently generated semantic vectors, using the get_embeddings tool before querying a database.
Must monitor token consumption in production services to prevent unexpected billing spikes; they use the get_usage tool constantly.
What Changes When You Connect
- Stop guessing which model to use. Use
list_modelsfirst to see every available version of the Yi LLM, then select the specific one you need for the task. - Control your spend right from your agent. The
get_usagetool lets you pull real-time token counts and balances before running expensive chat completions. - Build better search systems. Instead of basic keyword matching, use
get_embeddingsto convert company documents into semantic vectors, making RAG searches far more accurate. - Keep your outputs clean. Run any generated text through
check_moderationimmediately after the call. This stops policy violations from ever reaching the user. - Manage complex conversations easily. The
chat_completionstool handles persistent context, so you don't have to resend the entire chat history with every follow-up message.
Real-World Use Cases
Building a secure internal knowledge bot
A user needs an agent that answers questions based on private documents. First, they run get_embeddings on their 100 PDFs to create vectors. Then, when a question comes in, the agent uses those embeddings for retrieval and finally executes chat_completions to synthesize the answer. The whole flow is contained and verifiable.
Developing an automated content moderation pipeline
A platform needs to filter all user-submitted comments before saving them. The agent first runs a pre-check using check_moderation. If clean, it proceeds with the main task via chat_completions; otherwise, it flags the failure and stops.
Optimizing cost for an enterprise app
A developer suspects their service is running out of budget. They immediately call get_usage to see the current token count. This insight guides them to use a cheaper model found via list_models instead of defaulting to Yi-Large.
Integrating new LLM features into a client app
A team needs to test an entirely new feature that requires complex chat logic. Before writing any code, they call list_models to confirm the model ID exists and then use chat_completions in a sandboxed environment.
The Tradeoffs
Ignoring cost visibility
The developer just calls chat_completions repeatedly without checking the billing. They run 50,000 tokens in an hour and get a massive surprise bill later.
→
Always sandwich your main function call with checks. Before running chat completions, use get_usage. After you're done, run it again to confirm consumption. This keeps cost management visible.
Assuming model availability
The code fails because the developer hardcoded an old model name (like 'yi-v1') that no longer exists or is deprecated.
→
Never assume. Always start by calling list_models to get the current, active list of models. This prevents runtime failures and lets you use the right ID.
Sending raw user input directly
A malicious or inappropriate prompt gets passed straight into the chat completion engine without review.
→
Always filter first. Insert a call to check_moderation immediately before calling chat_completions. This gatekeeper tool keeps bad content out of your system.
When It Fits, When It Doesn't
Use this MCP Server if: You need reliable orchestration across multiple, distinct LLM functions (Chat, Embeddings, Moderation) and you must control the flow using API calls. Specifically, if cost tracking (get_usage) or model selection (list_models) is part of your core logic, this server gives you that necessary gatekeeping.
Don't use it if: You only need to run a simple chat query in isolation and don't care about moderation or usage. In those cases, a dedicated, single-purpose API might be simpler. Also, if your primary requirement is integrating with a platform other than the Yi ecosystem, you should look at a general-purpose LLM gateway that supports multiple vendors.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Lingyi Wanwu. 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
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
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 5 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manually checking model availability and usage stats is a time sink.
Today, if you want to know what models are available or how much money you’ve spent, you open three separate dashboards. One for billing, one for the model catalog, and another just for running tests. You copy IDs here, paste them there, and manually track tokens across spreadsheets.
With this MCP Server, all that data is exposed via simple tools. Run `list_models` to see every version available, then run `get_usage` to check your budget—all in the same agent workflow. It keeps the complexity visible, not hidden in a dashboard.
Using Lingyi Wanwu MCP Server for Chat Completions
Without this server, every time you want to update your chat logic—say, going from Yi-Large to Yi-34B—you have to write new boilerplate code and manually manage context window sizes.
Now, your agent handles the model switching. You just call `chat_completions` with the desired model ID, and it executes the logic. It makes model selection a simple function call, not a rewrite of your application's core logic.
Common Questions About Lingyi Wanwu MCP
How do I check my token usage using the `get_usage` tool? +
Call get_usage() in your agent workflow. It will return a JSON object detailing your current consumption and remaining balance for the Yi models.
What is the difference between chat completions and embeddings? +
Chat completions generate text based on prompts (like having a conversation). Embeddings (get_embeddings) convert text into numerical vectors, which are used by search engines to find semantic matches.
`list_models` tool: does it list all LLMs? +
No, list_models only lists the available Yi models. For a complete picture of every model on the market, you'll need to consult external documentation.
Can I use `check_moderation` before running `chat_completions`? +
Yes. It’s best practice to run a user prompt through check_moderation first. If the output is flagged, you stop the workflow and prevent the chat call from ever happening.
How do I handle rate limits when running `chat_completions`? +
The service manages standard API rate limits. If your agent exceeds the quota, it will receive a specific HTTP error code that tells you exactly how long to wait before retrying. You must implement exponential backoff in your workflow logic.
Does `get_embeddings` handle bilingual text, specifically Chinese characters? +
Yes, the embedding model is optimized for both English and Mandarin (EN/CN). You can pass combined English and Chinese texts together; it generates a single semantic vector that properly accounts for both language inputs.
If I use an outdated model name in `chat_completions`, how does `list_models` help? +
The list_models tool provides the definitive, currently active names and versions of all supported Yi models. Run this first to guarantee you are using the correct identifier before submitting any chat request.
What is the expected input format when running `check_moderation`? +
The tool expects either a single string or an array of strings in the payload. It checks all provided text elements against policy rules and returns a status flag for every item you send.
Which Yi model is best for complex reasoning? +
For complex reasoning and high-quality outputs, yi-large is recommended. For faster response times and cost efficiency, yi-medium or yi-spark are excellent alternatives.
Can I automatically retrieve my remaining account balance? +
Yes! Use the get_balance tool. Your agent will connect to the Lingyi Wanwu billing service and return your current remaining credits.
How do I list all the technical specs for the Yi models? +
Use the list_models tool. Your agent will retrieve a list of all models currently available on the platform, along with their IDs and capabilities.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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