How to Use the Fidelizador MCP in LlamaIndex

Q: How do I set up the Fidelizador MCP Server with LlamaIndex?

Install llama-index-tools-mcp and initialize BasicMCPClient. Convert the tools with mcptoolspec.totoollistasync() and pass them to your FunctionAgent to start querying.

Q: Can LlamaIndex index my Fidelizador contacts for semantic search?

Yes, you can use listcontacts to retrieve customer profiles and index them into a vector store. This lets your agent search through loyalty tiers and customer notes semantically.

Q: How does LlamaIndex handle campaign data from Fidelizador?

It uses listcampaigns and getcampaign to fetch raw campaign data. This data is structured into documents, indexed, and used to ground the agent's answers about marketing performance.

Q: Can I filter which tools the LlamaIndex agent can access?

Yes, you can use the allowedtools filter when setting up the tool spec. This lets you restrict the agent to read-only tools like listmailinglists if you don't want it modifying data.

Index your Fidelizador campaign data directly into LlamaIndex vector stores for smart retrieval.

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Connect Fidelizador MCP to LlamaIndex

Create your Vinkius account to connect Fidelizador 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 Fidelizador with LlamaIndex

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Turn loyalty data into searchable LlamaIndex knowledge

The `list_campaigns` tool pulls your active marketing metrics directly into LlamaIndex vector stores. With LlamaIndex, you can pull campaign metrics and index them directly into your vector database. Your agent can then query this index to find out which loyalty rewards worked best. It uses `get_campaign` to fetch details and grounds its answers in actual performance metrics, not guesses.

Query active contacts using LlamaIndex and this MCP Server

Using `list_contacts` allows your RAG pipelines to feed live customer profiles straight into your search index. The agent calls this tool to retrieve the current database and matches it against your customer success documents. If a customer asks about their status, LlamaIndex uses `update_contact` to adjust their profile based on the retrieved context, making sure their loyalty points are always accurate.

Build data-grounded LlamaIndex agents with MCP tools

The `list_mailing_lists` tool helps your LlamaIndex agent verify active customer groups before making recommendations. By passing the tools from this MCP Server to a LlamaIndex `FunctionAgent`, the agent only speaks using real data. The agent checks `list_mailing_lists` and `get_campaign` before suggesting a reward. It ensures every response is backed by your actual active marketing setups.

Setup guide

Set up Fidelizador 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 Fidelizador 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 Fidelizador tools.",
)
response = await agent.run("List recent Fidelizador 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 Fidelizador. 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 Fidelizador MCP in LlamaIndex

Install `llama-index-tools-mcp` and initialize `BasicMCPClient`. Convert the tools with `mcp_tool_spec.to_tool_list_async()` and pass them to your `FunctionAgent` to start querying.

Yes, you can use `list_contacts` to retrieve customer profiles and index them into a vector store. This lets your agent search through loyalty tiers and customer notes semantically.

It uses `list_campaigns` and `get_campaign` to fetch raw campaign data. This data is structured into documents, indexed, and used to ground the agent's answers about marketing performance.

Yes, you can use the `allowed_tools` filter when setting up the tool spec. This lets you restrict the agent to read-only tools like `list_mailing_lists` if you don't want it modifying data.

Your campaign metrics and list data are protected by Vinkius's zero-trust sandbox. The server runs in an ephemeral environment, so your LlamaIndex pipelines access the API securely without exposing raw credentials.

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