Adrecord MCP Server for LlamaIndex 5 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Adrecord as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Adrecord. "
"You have 5 tools available."
),
)
response = await agent.run(
"What tools are available in Adrecord?"
)
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 Adrecord MCP Server
Connect your Adrecord (powered by Adtraction) affiliate account to your AI agent to unlock professional performance marketing management. From auditing advertiser programs to tracking real-time sales and managing your media channels, your agent handles your affiliate ecosystem through natural conversation.
LlamaIndex agents combine Adrecord 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
- Program Management — List and retrieve details for available advertiser programs, including commission rates and rules
- Transaction Auditing — Retrieve detailed logs of sales and leads to monitor your marketing performance and ROI
- Channel Oversight — List and manage your registered media channels (websites, social media) across the network
- Product Feed Access — Retrieve and audit product data from advertisers to power your affiliate content
- Performance Insights — Quickly identify top-performing programs and track your pending earnings directly from chat
The Adrecord 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.
How to Connect Adrecord to LlamaIndex via MCP
Follow these steps to integrate the Adrecord 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 5 tools from Adrecord
Why Use LlamaIndex with the Adrecord MCP Server
LlamaIndex provides unique advantages when paired with Adrecord through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Adrecord tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Adrecord tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Adrecord, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Adrecord tools were called, what data was returned, and how it influenced the final answer
Adrecord + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Adrecord MCP Server delivers measurable value.
Hybrid search: combine Adrecord real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Adrecord 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 Adrecord for fresh data
Analytical workflows: chain Adrecord queries with LlamaIndex's data connectors to build multi-source analytical reports
Adrecord MCP Tools for LlamaIndex (5)
These 5 tools become available when you connect Adrecord to LlamaIndex via MCP:
get_product_feed
Fetch advertiser product data
get_program_details
Get program metadata
list_channels
List media channels
list_programs
List advertiser programs
list_transactions
Filterable by status. List sales and leads
Example Prompts for Adrecord in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Adrecord immediately.
"List all active advertiser programs I am partnered with."
"Show me all transactions from the last 30 days."
"Retrieve the product feed for program ID 5678."
Troubleshooting Adrecord MCP Server with LlamaIndex
Common issues when connecting Adrecord to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAdrecord + LlamaIndex FAQ
Common questions about integrating Adrecord 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 Adrecord with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
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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 Adrecord to LlamaIndex
Get your token, paste the configuration, and start using 5 tools in under 2 minutes. No API key management needed.