How to Use the BoxLock MCP in LlamaIndex

Q: Can LlamaIndex search BoxLock barcodes?

You can index the output of listbarcodes into your vector store. This allows the agent to find items by description or ID.

Index your BoxLock activity logs into LlamaIndex to query historical delivery data with natural language.

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Connect BoxLock MCP to LlamaIndex

Create your Vinkius account to connect BoxLock 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 BoxLock with LlamaIndex

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Ground LlamaIndex in BoxLock data

Run `list_activities` and pipe the results straight into a vector index. Your agent no longer guesses about delivery status. It searches your actual logs to answer questions about past package arrivals. You get precise answers backed by real API responses.

Search BoxLock logs with LlamaIndex

Use the MCP tool spec to turn `list_users` into a queryable knowledge source. You can ask your agent who accessed a specific site last week. It pulls the data, embeds it, and keeps it ready for semantic search. This beats manual log parsing every single time.

Build RAG apps with this MCP Server

Combine `list_locations` and `list_locks` into a unified index. Your LlamaIndex agent understands the layout of your entire operation. It maps barcodes to specific locks using the index you build. This creates a grounded reference point for all future security queries.

Setup guide

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

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Common questions about BoxLock MCP in LlamaIndex

Use the McpToolSpec to wrap the server tools. You then pass these tools to your FunctionAgent to begin building the index.

You can index the output of `list_barcodes` into your vector store. This allows the agent to find items by description or ID.

You re-run the tool calls to refresh your index with the latest logs. This keeps your RAG application current with physical world events.

Your indices are stored locally on your machine. The server only provides data during the active tool execution phase.

The server restricts access to activity logs via your Vinkius token. Your vector store only contains the specific data you choose to ingest.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

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