Kevel MCP Server for LangChain 11 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Kevel through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"kevel": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Kevel, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Kevel through native MCP adapters. Connect 11 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the Kevel MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 11 tools from Kevel via MCP
Why Use LangChain with the Kevel MCP Server
LangChain provides unique advantages when paired with Kevel through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Kevel MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Kevel queries for multi-turn workflows
Kevel + LangChain Use Cases
Practical scenarios where LangChain combined with the Kevel MCP Server delivers measurable value.
RAG with live data: combine Kevel tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Kevel, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Kevel tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Kevel tool call, measure latency, and optimize your agent's performance
Kevel MCP Tools for LangChain (11)
These 11 tools become available when you connect Kevel to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Kevel to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersKevel + LangChain FAQ
Common questions about integrating Kevel MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.