SambaNova (AI Inference) MCP. Run Llama 3 and DeepSeek models at record-breaking speed.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
SambaNova (AI Inference) MCP Server provides high-speed access to state-of-the-art open models like Llama 3 and DeepSeek. It runs inference using SambaNova's SN40L chips, giving you record token speeds that standard cloud APIs can't match.
Use it for chat completions, generating vector embeddings, or forcing model output into structured formats.
What your AI agents can do
Create chat completion
Uses SambaNova models to generate conversational text based on a chat history, compatible with OpenAI's Chat Completions API format.
Create embedding
Generates high-dimensional vector embeddings for given text inputs using the specialized SambaStack service.
Create response
Processes a request and returns model output items that are strictly typed, ensuring reliable data structure for agentic workflows.
Use create_chat_completion to get high-quality conversational text from state-of-the-art models like Llama 3.3 and DeepSeek.
Call create_embedding to generate numerical vectors for any piece of text, making it ready for vector databases or RAG systems.
Use create_response to make sure the model output is always in a predictable, typed JSON format, which reliable agents need.
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SambaNova (AI Inference) MCP Server: 3 Model Inference Tools
Use these three core tools to manage the entire LLM workflow—from generating raw text responses to creating reliable structured data and vector embeddings.
019e5d52create chat completion
Uses SambaNova models to generate conversational text based on a chat history, compatible with OpenAI's Chat Completions API format.
019e5d52create embedding
Generates high-dimensional vector embeddings for given text inputs using the specialized SambaStack service.
019e5d52create response
Processes a request and returns model output items that are strictly typed, ensuring reliable data structure for agentic workflows.
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
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Make Your AI Do More
Start with SambaNova (AI Inference), 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
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Listen up. This ain't your grandma's API connection. You're hooking your agent up to SambaNova Cloud through this MCP Server, giving you access to some of the fastest open models out there—stuff like Llama 3.3 and DeepSeek. It runs inference using SN40L chips, meaning you get token speeds that standard cloud APIs just can't touch.
You're building real-time apps here; latency is everything.
When your agent needs to chat, you use create_chat_completion. This tool lets you generate high-quality conversational text using state-of-the-art models. You feed it a chat history, and it spits out the next part of the conversation, keeping that OpenAI Chat Completions API format intact so your client doesn't sweat the details.
Need to turn unstructured text into something usable? Call create_embedding. This tool generates high-dimensional numerical vectors for any piece of text you throw at it, using the specialized SambaStack service. These vectors are exactly what you need when you build a Retrieval-Augmented Generation (RAG) system or integrate with a vector database.
You get the numbers, period.
And here’s the critical part for reliable agents: structured output. When you use create_response, you force the model to spit out items that are strictly typed. This isn't just getting text; this is guaranteeing a predictable data structure—like a JSON object with specific keys and values—which your agent needs to actually perform an action, not just talk about it.
The whole thing works by having your client call one of these tools. You subscribe to the server, give your AI agent permission, and bam—you get the result instantly because of that SN40L infrastructure under the hood. It's pure speed for complex tasks.
You're building anything where throughput matters, right? If you can't afford lag or low processing power when handling a steady stream of requests, this is your ticket. You bypass standard LLM provider bottlenecks and get a faster, more robust way to run open-source models directly from your agent.
How SambaNova (AI Inference) MCP Works
- 1 Subscribe to the SambaNova (AI Inference) MCP Server and input your API key.
- 2 Your AI agent calls one of the defined tools (e.g.,
create_chat_completion) with specific parameters. - 3 The server uses SambaNova's SN40L chips to process the request and sends back the result, whether it’s text, a vector, or structured data.
The bottom line is you connect your client directly to high-performance compute for instant model results.
Who Is SambaNova (AI Inference) MCP For?
Backend engineers and AI developers building anything that requires real-time, reliable LLM output. This server solves the problem of latency and inconsistent data formats common when relying on general API endpoints.
Building proof-of-concept applications that require low-latency inference and high throughput for conversational or retrieval tasks.
Integrating complex, multi-step AI logic into production services where the output must be a predictable JSON object, not just freeform text.
Building large-scale knowledge bases by generating millions of embeddings in bulk for vector search systems.
What Changes When You Connect
- Speed: Achieve rock-bottom latency. SambaNova's SN40L chips deliver tokens per second speeds that make standard cloud providers look slow.
- Reliability: Use
create_responseto guarantee your model output is always structured and typed, which stops agents from breaking over unexpected text formats. - Scale: Generate millions of vectors quickly. The
create_embeddingtool handles large-scale data indexing for RAG at high throughput. - Flexibility: Access multiple top models (Llama 3, DeepSeek) through a unified interface via
create_chat_completion, letting you pick the best model for the job. - Cost Control: It offers developers a faster and often more cost-effective alternative to standard LLM APIs without sacrificing performance.
Real-World Use Cases
Building a Real-Time Q&A Chatbot
The user uploads documents and needs a chatbot. Instead of just using chat completions, the agent first calls create_embedding to index the docs. When asked a question, it runs the query through create_embedding again, finds the most relevant chunks, then passes that context to create_chat_completion for an accurate, grounded answer.
Developing Autonomous Workflow Agents
An agent needs to process user input and output three specific fields: 'User ID,' 'Task Type,' and 'Priority.' Instead of asking the LLM to just write text (and hoping it's formatted correctly), the agent calls create_response, forcing the model to return a clean, typed JSON object every single time.
Comparing Model Performance
A developer needs to see how Llama 3 handles complex reasoning compared to DeepSeek. They can use create_chat_completion multiple times with the same prompt and only swap out the model name, letting them benchmark performance side-by-side in a single workflow.
Large-Scale Knowledge Base Indexing
A data team has 10,000 articles. They can't manually process every one. They use create_embedding repeatedly across all documents to generate a massive index of vectors for later retrieval.
The Tradeoffs
Treating LLM output as reliable data
The agent calls create_chat_completion and expects the result to always be JSON, but if the model hallucinates a stray comma or changes its format slightly, the downstream code breaks.
→
Don't rely on raw chat completions for structured input. Always use create_response. This tool forces the output into specific data types, so your agent won't crash when the LLM gets creative.
Running vector searches manually
The developer copies a user query and tries to pass it directly into create_chat_completion hoping the model will 'search' for context, which it can't do reliably.
→
You must convert text to numbers first. Before any search or RAG step, call create_embedding. This turns your plain text query into a vector that your database actually understands.
Using general APIs for specialized tasks
Trying to run computationally heavy model inference on a basic endpoint when you need the speed of SN40L.
→ For maximum performance, especially with large models like Llama 3.3-70B, use this server. It's built specifically for high-throughput, low-latency inference.
When It Fits, When It Doesn't
Use SambaNova (AI Inference) MCP Server if your application needs ultra-low latency and very high throughput when calling LLMs or generating embeddings. Specifically: 1) If you need the model output to be a guaranteed data structure, use create_response. 2) If your app relies on searching documents against a knowledge base, start by calling create_embedding for all inputs. 3) If you just need general chat responses at record speed, stick with create_chat_completion. DON'T use this server if: You are building simple local scripts that run occasionally and latency is zero-percent of your concern. In those cases, a basic API call might suffice. But for any production system handling real users, the performance edge here is critical.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by SambaNova. 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 server provides 3 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Waiting on AI responses shouldn't feel like dial-up internet.
Today, making an LLM work means hitting a bunch of endpoints. You send a prompt for text, then maybe another call to generate embeddings, and if you need the output to be JSON, you hope the model is nice enough to format it right. It's slow, and every step introduces potential failure points.
With this MCP server, your agent talks to SambaNova directly. You make one call—say, `create_chat_completion`—and we handle the blazing-fast processing on SN40L chips. The result is back instantly, giving you a single source of truth for model output.
The `create_response` tool lets your agent stop guessing and start building.
Before this server, if an agent needed to extract three facts from text, it would call a general chat completion. The output might be: 'The car was red. It cost $50k. John bought it.' Your code then has to use regex or complex parsing to reliably pull out the color, price, and name.
Now, your agent calls `create_response` and specifies the schema: {color: string, price: number, buyer: string}. The model is forced to fit that structure. You get clean, predictable data every time.
Common Questions About SambaNova (AI Inference) MCP
How does create_chat_completion differ from a standard API call? +
It runs inference on SambaNova's specialized SN40L chips, giving you much higher tokens-per-second speeds. This means less waiting time and better performance for large models like Llama 3.
Which tool should I use if my goal is Retrieval-Augmented Generation (RAG)? +
You need both create_embedding and create_chat_completion. First, use create_embedding to turn your documents into vectors. Then, pass the retrieved context via create_chat_completion for the final answer.
Can I use create_response even if I don't need JSON? +
No. The whole point of create_response is to enforce structure. It forces type safety, which means you get reliable, structured data—that’s what it does best.
Is this server compatible with my existing Python agent setup? +
Yes. The tools are designed to be called by any MCP-compatible client (Claude, Cursor, etc.), meaning you can integrate the functions into your existing Python or JavaScript agents.
How do I authenticate when using create_chat_completion? +
You use your SambaNova Cloud API Key for authentication. Your AI client passes this key as a secure header, giving you direct access to run the high-performance models. This keeps your connection isolated and authenticated.
What is the maximum input text size when calling create_embedding? +
The embedding tool accepts up to a specified token limit per request, which varies by model (e.g., E5-Mistral-7B-Instruct). Always check the documentation for that specific model's exact token count before sending data.
Does using create_chat_completion have strict rate limits? +
SambaNova’s infrastructure is designed to maintain low latency and high throughput under heavy load. While standard API usage limits apply, the SN40L chips minimize bottlenecks when running complex inference jobs.
What happens if I call create_response with incorrect data types? +
The system handles type validation errors by returning a specific HTTP status code and an error message. Your agent can catch this failure and correct the input payload before retrying the structured output request.
Which models are available for chat completions? +
You can use create_chat_completion with models like Meta-Llama-3.3-70B-Instruct, DeepSeek-V3.1, and MiniMax-M2.5 for high-speed text generation.
Can I generate embeddings for my RAG pipeline? +
Yes! Use the create_embedding tool with models like E5-Mistral-7B-Instruct to create vectorized representations of your text data.
What is the difference between create_chat_completion and create_response? +
create_chat_completion follows the standard OpenAI chat format, while create_response is a stateless API designed specifically for agentic workflows, returning typed output items.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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