Swarm MCP Server for LlamaIndexGive LlamaIndex instant access to 5 tools to Get Customer Balance, List Available Rewards, List Customer Vouchers, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Swarm as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The Swarm app connector for LlamaIndex is a standout in the Marketing Automation category — giving your AI agent 5 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Swarm. "
"You have 5 tools available."
),
)
response = await agent.run(
"What tools are available in Swarm?"
)
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 Swarm MCP Server
Connect your Swarm loyalty account to any AI agent and simplify how you manage customer rewards, award points for transactions, and handle redemptions through natural conversation.
LlamaIndex agents combine Swarm tool responses with indexed documents for comprehensive, grounded answers. Connect 5 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
- Point Management — Retrieve real-time point balances and loyalty tiers for specific customer IDs.
- Transaction Processing — Programmatically award points to customers by registering sale amounts and product data via AI.
- Reward Redemption — Convert customer points into discount vouchers or specific rewards and list all active vouchers.
- Catalog Discovery — Browse available rewards and check eligibility for specific customers instantly.
- Voucher Oversight — List and query all unused discount codes currently assigned to a customer's profile.
- Loyalty Lifecycle — Manage the entire customer reward journey directly from Claude, Cursor, or any MCP client.
The Swarm MCP Server exposes 5 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.
All 5 Swarm tools available for LlamaIndex
When LlamaIndex connects to Swarm through Vinkius, your AI agent gets direct access to every tool listed below — spanning loyalty-programs, rewards-management, customer-retention, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Check customer loyalty points
List redeemable rewards
List active customer vouchers
Process a sale and award points
Redeem points for a reward
Connect Swarm to LlamaIndex via MCP
Follow these steps to wire Swarm into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Swarm MCP Server
LlamaIndex provides unique advantages when paired with Swarm through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Swarm tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Swarm tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Swarm, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Swarm tools were called, what data was returned, and how it influenced the final answer
Swarm + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Swarm MCP Server delivers measurable value.
Hybrid search: combine Swarm real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Swarm 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 Swarm for fresh data
Analytical workflows: chain Swarm queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Swarm in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Swarm immediately.
"What is the point balance for customer 'cust_10293'?"
"Award points for a $150 purchase to customer 'cust_88231'."
"Show me all available rewards I can claim with 500 points."
Troubleshooting Swarm MCP Server with LlamaIndex
Common issues when connecting Swarm to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpSwarm + LlamaIndex FAQ
Common questions about integrating Swarm MCP Server with LlamaIndex.