AdButler 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 AdButler 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 MCP SERVER
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 AdButler. "
"You have 5 tools available."
),
)
response = await agent.run(
"What tools are available in AdButler?"
)
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 AdButler MCP Server
Connect your AdButler account to your AI agent to unlock professional ad serving management and real-time reporting. From auditing publisher inventory to monitoring campaign delivery and analyzing click-through rates (CTR), your agent handles your ad operations through natural conversation.
LlamaIndex agents combine AdButler 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
- Inventory Management — List publishers and ad zones to maintain full control over where your advertisements are displayed
- Campaign Oversight — List and retrieve details for self-serve campaigns, including statuses and targeting rules
- Performance Reporting — Retrieve instant statistics on impressions, clicks, and revenue across your entire network
- Creative Auditing — List and manage ad creative assets to ensure your visual content is always up-to-date
- Revenue Optimization — Quickly identify top-performing zones or underdelivering campaigns directly from your chat interface
The AdButler 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 AdButler to LlamaIndex via MCP
Follow these steps to integrate the AdButler 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 AdButler
Why Use LlamaIndex with the AdButler MCP Server
LlamaIndex provides unique advantages when paired with AdButler through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine AdButler tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain AdButler tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query AdButler, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what AdButler tools were called, what data was returned, and how it influenced the final answer
AdButler + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the AdButler MCP Server delivers measurable value.
Hybrid search: combine AdButler real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query AdButler 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 AdButler for fresh data
Analytical workflows: chain AdButler queries with LlamaIndex's data connectors to build multi-source analytical reports
AdButler MCP Tools for LlamaIndex (5)
These 5 tools become available when you connect AdButler to LlamaIndex via MCP:
get_performance_report
Provide a specific metric type and optional dates. Retrieve aggregated ad performance metrics (impressions, clicks, CTR) across zones and campaigns in AdButler
list_campaigns
Retrieve a list of active and pending self-serve advertising campaigns in AdButler
list_creatives
Retrieve the library of ad assets (banners, videos) stored in your AdButler account
list_publishers
Retrieve the full list of publishers managing ad inventory in your AdButler network
list_zones
Requires Publisher ID. Retrieve the active ad zones (placements) linked to a specific AdButler publisher
Example Prompts for AdButler in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with AdButler immediately.
"List all publishers in my AdButler network."
"Show me the performance report for the last 7 days."
"List all active zones for publisher ID 12345."
Troubleshooting AdButler MCP Server with LlamaIndex
Common issues when connecting AdButler to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAdButler + LlamaIndex FAQ
Common questions about integrating AdButler 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 AdButler with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
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 AdButler to LlamaIndex
Get your token, paste the configuration, and start using 5 tools in under 2 minutes. No API key management needed.