How to Use the Nabis MCP in LlamaIndex

Q: How does this setup prevent LLM hallucinations?

Your agent relies on the structured output of tools like getretailer to answer questions. It uses actual database records instead of guessing details about your wholesale clients.

Index Nabis wholesale metrics into LlamaIndex vector stores to ground your queries in live cannabis distribution data.

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Connect Nabis MCP to LlamaIndex

Create your Vinkius account to connect Nabis 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 Nabis with LlamaIndex

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Build searchable LlamaIndex inventory databases

The `list_inventory` tool retrieves current stock levels across your entire distribution network. LlamaIndex takes this raw data and indexes it directly into your vector store, allowing your agent to run semantic searches over physical inventory using this MCP Server. Users can query the system using natural language to find out which brands are running low. The engine answers based on live API data, preventing hallucinations about what is actually on the shelves.

Analyze historical order records

The `list_orders` tool provides a complete history of wholesale cannabis transactions. Your RAG pipeline ingests these records using our MCP integration to build a searchable knowledge base of customer buying habits. Combining this with `get_order` allows you to retrieve deep details on specific shipments. Your agent can spot seasonal buying trends by comparing past orders with current requests.

Reconcile invoices against warehouse data

The `list_invoices` tool pulls financial records directly from the distribution platform. Your indexer pairs these documents with physical inventory counts to verify that every shipped box matches a paid invoice. Running `get_warehouse_count` alongside this check ensures your physical counts match your ledger. Discrepancies get flagged immediately before they impact your tax reporting.

Setup guide

Set up Nabis 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 Nabis 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 Nabis tools.",
)
response = await agent.run("List recent Nabis data")

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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 Nabis. 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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Common questions about Nabis MCP in LlamaIndex

Install llama-index-tools-mcp and initialize the basic client. You then convert the server tools into a tool spec and pass them to your FunctionAgent.

No, the framework queries the live server on demand to ensure your index contains fresh data. You can choose to persist the resulting vector embeddings locally if you want to search past sessions.

Yes, the tool spec wrapper supports an allowed tools filter. You can restrict your agent to safe read-only tools like `get_days_off` while blocking writing actions.

Your agent relies on the structured output of tools like `get_retailer` to answer questions. It uses actual database records instead of guessing details about your wholesale clients.

Vinkius processes all tool execution inside secure, isolated runtime environments that leave zero footprint. Your warehouse counts, retailer details, and financial invoices are transmitted via encrypted channels and never cached.

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