How to Use the Bloomreach MCP in LlamaIndex

Q: Can I build a semantic search index over Bloomreach catalogs using LlamaIndex?

Yes, you can run getcatalogitems to pull items and load them directly into LlamaIndex Document objects. From there, you vector-index the items so your agent can search catalog data semantically.

Q: How do LlamaIndex agents use Bloomreach segment data?

The agent calls listsegments to retrieve active customer groups, then indexes those definitions to answer user questions about current marketing targets. This grounds the agent's responses in real-time audience definitions.

Q: Is sensitive customer property data cached by LlamaIndex?

No, retrieved data from getcustomerproperties is only stored in memory during index generation or query execution unless you explicitly write it to a local vector database. Vinkius processes all MCP tool calls in isolated, zero-trust sandboxes to keep your marketing data private.

Index Bloomreach marketing data into LlamaIndex vector stores for ground-truth semantic search.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Bloomreach MCP to LlamaIndex

Create your Vinkius account to connect Bloomreach 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.

GDPR Free for Subscribers

Setup Bloomreach with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build queryable indexes from Bloomreach catalogs

The `list_catalogs` tool pulls raw catalog structures directly into LlamaIndex's document ingestion pipeline for immediate indexing. Your agent queries this index to find specific product schemas without making repeated API calls. Once indexed, you can run semantic searches over catalog metadata retrieved by `get_catalog_items`. This keeps your RAG application grounded in actual product data instead of relying on outdated model memory.

Ground LlamaIndex queries in live customer segments

The `list_segments` tool feeds live user group definitions directly into LlamaIndex's vector store indexers. Your agent compares these active segments with incoming natural language queries to identify the correct target audience. By indexing the output of `list_segmentations` alongside your customer data, you create a searchable knowledge base of marketing targets. This ensures your agent references real segment definitions during query synthesis.

Feed Bloomreach event types to LlamaIndex agents

Your LlamaIndex agent executes `list_event_types` to pull live tracking schemas directly into its context window. It connects using the MCP Server config. This direct access stops your agent from guessing event names or properties during complex RAG operations. You get exact, live event data mapped directly to your vector index nodes.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Bloomreach. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Bloomreach now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Bloomreach MCP in LlamaIndex

Yes, you can run `get_catalog_items` to pull items and load them directly into LlamaIndex Document objects. From there, you vector-index the items so your agent can search catalog data semantically.

The agent calls `list_segments` to retrieve active customer groups, then indexes those definitions to answer user questions about current marketing targets. This grounds the agent's responses in real-time audience definitions.

Yes, you can use the asynchronous tool execution methods in LlamaIndex to run calls like `list_campaigns` in parallel. This speeds up the process of building your marketing knowledge index with this MCP Server.

You define an allowed tools list when initializing the MCP client wrapper. This restricts your LlamaIndex agent to specific operations, like only allowing catalog reads while blocking customer property access.

No, retrieved data from `get_customer_properties` is only stored in memory during index generation or query execution unless you explicitly write it to a local vector database. Vinkius processes all MCP tool calls in isolated, zero-trust sandboxes to keep your marketing data private.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript