How to Use the Friendbuy MCP in LlamaIndex

Q: Does LlamaIndex support tool filtering?

It does. You can use an allowedtools list to restrict which Friendbuy operations your agent can execute during a query, preventing unauthorized modifications.

Index your referral performance data into LlamaIndex. Ground your RAG applications in live Friendbuy metrics.

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Connect Friendbuy MCP to LlamaIndex

Create your Vinkius account to connect Friendbuy 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 Friendbuy with LlamaIndex

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Semantic search over Friendbuy data

Use `list_referral_rewards` to extract reward history and index it into a vector store. This turns your raw referral data into a searchable knowledge base for your AI. Now, you can ask your agent about specific reward patterns. It retrieves the actual data, providing grounded answers instead of guessing based on outdated snapshots.

Contextualize referral signups in LlamaIndex

Pass `track_conversion_signup` events into your ingestion pipeline to keep your index updated. Your RAG app gains awareness of every new referral as it happens. It bridges the gap between static docs and live user behavior. The agent sees the signup event and immediately updates the vector index, keeping your knowledge base current.

Filter and query via MCP tool specs

Utilize `get_referral_code_status` to filter which data points enter your index. By setting specific inclusion criteria, you only store relevant, active code information. This keeps your index clean and focused. You avoid polluting your vector store with expired records, ensuring the agent retrieves only high-quality, actionable data.

Setup guide

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

The MCP tool spec converts API outputs into a format LlamaIndex can process. It then indexes these results into your vector store for semantic retrieval.

Absolutely. By passing the tool list to your FunctionAgent, you allow the system to call the API, fetch data, and then perform retrieval-augmented generation on that specific data.

The server transmits data over encrypted tunnels using your unique endpoint token. Only your authenticated agents can trigger the retrieval of raw referral records.

It does. You can use an allowed_tools list to restrict which Friendbuy operations your agent can execute during a query, preventing unauthorized modifications.

Set your ingestion pipeline to trigger on a schedule or via webhooks. This ensures your vector store reflects the latest purchase and reward data from your program.

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