How to Use the LeadDyno MCP in LlamaIndex

Index LeadDyno data in LlamaIndex. Turn affiliate records into a searchable knowledge base for your AI.

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Connect LeadDyno MCP to LlamaIndex

Create your Vinkius account to connect LeadDyno 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 LeadDyno with LlamaIndex

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Search LeadDyno history in LlamaIndex

Pull your purchase and lead history into a vector index. Use `list_purchases` to grab your transaction logs and convert them into searchable embeddings. You can then ask your agent questions about specific sales cycles. It answers by referencing the exact data recorded in your affiliate program.

Query affiliate performance metrics

Index your affiliate list and commission history for quick retrieval. `list_affiliates` provides the raw data that LlamaIndex stores for semantic search. This lets you run queries like finding top performers based on historical commission data. Your index stays current as you trigger these calls to refresh the data.

Create RAG pipelines with this MCP Server

Build a knowledge-augmented agent that uses real-time API data. By calling `get_affiliate` on demand, the agent fetches fresh details to ground its answers. It combines these live results with your static documents. This ensures your AI provides accurate insights based on what is happening in your program right now.

Setup guide

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

You fetch the data using the tools and index the output. Once the data is in your vector store, your agent performs semantic searches against it.

It will. You pull your purchase history into an index and ask your agent to identify patterns or trends in your sales data.

You control the refresh cycle. Call the list tools whenever you need to update your index with the latest affiliate or purchase information.

It handles the requests as you trigger them. You should batch your calls if you are indexing thousands of records to keep within API limits.

Your affiliate records are encrypted in transit via HTTPS. They are stored only in your local vector index and never shared with the Vinkius hosting layer.

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