Referrizer MCP Server for LlamaIndexGive LlamaIndex instant access to 11 tools to Create Contact, Get Campaign, Get Contact Details, and more

Name: Referrizer
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Referrizer as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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The Referrizer MCP Server for LlamaIndex is a standout in the Marketing Automation category — giving your AI agent 11 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

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python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Referrizer. "
            "You have 11 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Referrizer?"
    )
    print(response)

asyncio.run(main())

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About Referrizer MCP Server

Connect your Referrizer account to any AI agent and simplify your referral marketing, customer loyalty, and retention workflows through natural conversation.

LlamaIndex agents combine Referrizer tool responses with indexed documents for comprehensive, grounded answers. Connect 11 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Contact Management — List all customers and contacts, retrieve detailed profile metadata, and monitor loyalty points and status
Referral Tracking — Access a history of successful and pending customer referrals to understand your word-of-mouth growth
Campaign Control — Query past and active marketing automation campaigns to monitor your outreach performance
Loyalty Rewards — List available rewards and incentives to choose the right context for each interaction
Direct Enrollment — Register new contacts and customers programmatically directly from your agent

The Referrizer MCP Server exposes 11 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 11 Referrizer tools available for LlamaIndex

When LlamaIndex connects to Referrizer through Vinkius, your AI agent gets direct access to every tool listed below — spanning referral-marketing, loyalty-programs, customer-retention, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

create

Update a contact profile

Connect Referrizer to LlamaIndex via MCP

Follow these steps to wire Referrizer into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 11 tools from Referrizer

Why Use LlamaIndex with the Referrizer MCP Server

LlamaIndex provides unique advantages when paired with Referrizer through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Referrizer tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Referrizer tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Referrizer, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Referrizer tools were called, what data was returned, and how it influenced the final answer

Referrizer + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Referrizer MCP Server delivers measurable value.

Hybrid search: combine Referrizer real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Referrizer to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Referrizer for fresh data

Analytical workflows: chain Referrizer queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for Referrizer in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Referrizer immediately.

"List all active contacts in Referrizer."

"Show me the ROI analysis for all active loyalty and referral campaigns."

"Create a new contact and enroll them in the Birthday Rewards campaign."

Troubleshooting Referrizer MCP Server with LlamaIndex

Common issues when connecting Referrizer to LlamaIndex through Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Referrizer + LlamaIndex FAQ

Common questions about integrating Referrizer MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Referrizer tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.

Does LlamaIndex support async MCP calls?

Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.

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