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How to Use the Adtraction MCP in LlamaIndex

Index your affiliate performance data directly into LlamaIndex to build RAG apps that query Adtraction stats.

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LlamaIndex

Connect Adtraction MCP to LlamaIndex

Create your Vinkius account to connect Adtraction 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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Index Adtraction stats for LlamaIndex RAG

Use `get_stats_by_program` to generate searchable knowledge. LlamaIndex ingests the tool output and stores it in your vector index for deep semantic retrieval. Stop guessing performance trends. Ask your agent questions about past periods and get answers grounded in your actual network data.

Query affiliate programs with LlamaIndex

Call `list_programs` to seed your knowledge base. The agent maps your active campaigns and stores the program metadata for future reference. When you ask about campaign alignment, LlamaIndex pulls from the indexed program list. It creates a unified view of your entire affiliate footprint.

Audit transactions using LlamaIndex search

The `list_transactions` tool fetches your raw activity. Indexing this data allows you to search for specific conversion patterns across your entire history. Your agent cross-references these transactions against your documentation. It avoids hallucinations by relying on the live data you indexed.

Setup guide

Set up Adtraction MCP in LlamaIndex

Prerequisites

  • Python 3.10+ installed
  • llama-index-tools-mcp package
  • Active Vinkius subscription with a valid endpoint token
  1. 1

    Install dependencies

    Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.

  2. 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. 3

    Convert to LlamaIndex tools

    Call mcp_tool_spec.to_tool_list_async() to convert all Adtraction MCP tools into native FunctionTool objects that any LlamaIndex agent can use.

  4. 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 Adtraction tools.",
)
response = await agent.run("List recent Adtraction data")

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Adtraction. 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 Adtraction MCP in LlamaIndex

Use the MCP tool spec to convert the server tools into a list. Pass these to your FunctionAgent to enable direct API interaction.
LlamaIndex indexes the tool outputs into a vector store. This makes your historical data searchable and persistent for future queries.
Yes, use the allowed_tools filter during setup. This restricts your agent to specific operations like listing channels or checking program stats.
You re-run the tool calls to update your index. The framework handles the ingestion process, keeping your grounded knowledge current.
Access is restricted to your authenticated session. Your sensitive affiliate IDs and revenue figures are isolated within the Vinkius ephemeral sandbox.

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