Zinrelo MCP Server for LlamaIndex 9 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Zinrelo as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 Zinrelo. "
"You have 9 tools available."
),
)
response = await agent.run(
"What tools are available in Zinrelo?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Zinrelo MCP Server
Connect your Zinrelo account to any AI agent to automate your loyalty and rewards operations. This MCP server enables your agent to interact with loyalty members, award points for activities or purchases, and manage reward redemptions directly from natural language.
LlamaIndex agents combine Zinrelo tool responses with indexed documents for comprehensive, grounded answers. Connect 9 tools through the 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
- Member Management — Enroll new customers and retrieve detailed loyalty profiles, including tier status and point balances
- Points Automation — Award points for custom activities or purchase transactions instantly
- Reward Processing — Redeem points for rewards and manage manual point deductions when necessary
- Activity Auditing — List comprehensive transaction histories for any loyalty member to track earnings and usage
- Program Oversight — Access high-level loyalty settings and account configuration details
The Zinrelo MCP Server exposes 9 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Zinrelo to LlamaIndex via MCP
Follow these steps to integrate the Zinrelo MCP Server with LlamaIndex.
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 9 tools from Zinrelo
Why Use LlamaIndex with the Zinrelo MCP Server
LlamaIndex provides unique advantages when paired with Zinrelo through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Zinrelo tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Zinrelo tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Zinrelo, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Zinrelo tools were called, what data was returned, and how it influenced the final answer
Zinrelo + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Zinrelo MCP Server delivers measurable value.
Hybrid search: combine Zinrelo real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Zinrelo 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 Zinrelo for fresh data
Analytical workflows: chain Zinrelo queries with LlamaIndex's data connectors to build multi-source analytical reports
Zinrelo MCP Tools for LlamaIndex (9)
These 9 tools become available when you connect Zinrelo to LlamaIndex via MCP:
award_points_activity
Award points for a custom activity
award_points_purchase
Award points for a purchase
deduct_points
Manually deduct points from a user
enroll_member
Enroll or update a loyalty member
get_loyalty_settings
Get account loyalty settings
get_member_details
Get details for a specific loyalty member
list_loyalty_members
List all loyalty program members
list_member_transactions
List transaction history for a member
redeem_reward
g., coupon). Redeem points for a reward
Example Prompts for Zinrelo in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Zinrelo immediately.
"Show me the loyalty profile for 'customer@example.com'."
"Award 500 points to 'jane.doe@example.com' for a $50.00 purchase."
"List all transactions for 'john.smith@example.com'."
Troubleshooting Zinrelo MCP Server with LlamaIndex
Common issues when connecting Zinrelo to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpZinrelo + LlamaIndex FAQ
Common questions about integrating Zinrelo MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Zinrelo with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Zinrelo to LlamaIndex
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.