Kevel MCP Server for LlamaIndex 11 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Kevel 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 Kevel. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in Kevel?"
)
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 Kevel MCP Server
Connect your Kevel (formerly Adzerk) account to any AI agent to streamline your ad serving operations. This MCP server allows your agent to manage advertisers, campaigns, flights, and inventory sites directly through natural language.
LlamaIndex agents combine Kevel tool responses with indexed documents for comprehensive, grounded answers. Connect 11 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
- Campaign Management — List and retrieve detailed configurations for campaigns and flights
- Advertiser Oversight — Query and manage advertising entities and their metadata
- Inventory Control — List and inspect sites, zones, and channels to manage your ad placements
- Creative Audit — Access a comprehensive list of ad creatives and individual ad instances
- Format Exploration — List supported ad types and sizes to ensure correct technical implementations
The Kevel MCP Server exposes 11 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 Kevel to LlamaIndex via MCP
Follow these steps to integrate the Kevel 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 11 tools from Kevel
Why Use LlamaIndex with the Kevel MCP Server
LlamaIndex provides unique advantages when paired with Kevel through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Kevel tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Kevel tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Kevel, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Kevel tools were called, what data was returned, and how it influenced the final answer
Kevel + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Kevel MCP Server delivers measurable value.
Hybrid search: combine Kevel real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Kevel 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 Kevel for fresh data
Analytical workflows: chain Kevel queries with LlamaIndex's data connectors to build multi-source analytical reports
Kevel MCP Tools for LlamaIndex (11)
These 11 tools become available when you connect Kevel to LlamaIndex via MCP:
get_advertiser
Get details for a specific advertiser
get_campaign
Get details for a specific campaign
list_ad_types
g., banner, native). List available ad types
list_ads
List all ads
list_advertisers
List all advertisers in Kevel
list_campaigns
List all campaigns
list_channels
List all channels
list_creatives
) uploaded to the account. List all creatives
list_flights
List all flights
list_sites
List all sites
list_zones
List all zones
Example Prompts for Kevel in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Kevel immediately.
"Show me all active campaigns in Kevel."
"List all ad zones for the site with ID 12345."
"What ad types are supported in my Kevel account?"
Troubleshooting Kevel MCP Server with LlamaIndex
Common issues when connecting Kevel to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpKevel + LlamaIndex FAQ
Common questions about integrating Kevel 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 Kevel 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 Kevel to LlamaIndex
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.