How to Use the Modelbit (ML Model Deployments) MCP in LlamaIndex

Index live ML model predictions from Modelbit directly into your LlamaIndex knowledge base.

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Connect Modelbit (ML Model Deployments) MCP to LlamaIndex

Create your Vinkius account to connect Modelbit (ML Model Deployments) to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Modelbit (ML Model Deployments) with LlamaIndex

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Feed live predictions into your index

The `get_inference` tool retrieves real-time model outputs and lets you index them on the fly. This prevents your RAG applications from relying on stale, pre-computed data. Your LlamaIndex agent calls the model, gets the raw prediction, and structures it as a document node. This node becomes immediately searchable for subsequent user queries.

Ground your RAG pipeline in actual model data

Using this MCP Server ensures your agent's answers are backed by live mathematical models. It queries the endpoint to verify facts before generating text. This workflow stops hallucinations in their tracks. Instead of guessing a user's risk score, the agent runs the calculation through the server and outputs the real number.

Asynchronous tool execution for high throughput

The tool list is loaded asynchronously to keep your search indexes fast. You won't block the main thread while waiting for remote model deployments on this MCP Server to respond. LlamaIndex handles these async calls natively. It means your agent can process multiple user queries and model inferences concurrently without bottlenecking your app.

Setup guide

Set up Modelbit (ML Model Deployments) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Modelbit (ML Model Deployments) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Modelbit (ML Model Deployments) tools.",
)
response = await agent.run("List recent Modelbit (ML Model Deployments) data")

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Common questions about Modelbit (ML Model Deployments) MCP in LlamaIndex

Use the MCP tool spec package to wrap the client. Convert it to a tool list and pass it to your FunctionAgent.

Yes. You capture the output from the inference tool and wrap it in a Document object to insert it into your vector store.

Yes. The underlying MCP Server integration uses async tool calls to prevent blocking your index retrieval pipelines.

You configure this at the workspace level or by filtering the allowed tools in your agent setup. This prevents unauthorized model executions.

Your data travels over encrypted TLS connections directly to the isolated runtime. No raw input features or model parameters are written to persistent disk on our proxy.

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