LinkedIn Ads MCP Server for LangChain 6 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect LinkedIn Ads through the 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({
"linkedin-ads": {
"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 LinkedIn Ads, 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 LinkedIn Ads MCP Server
Connect your LinkedIn Ads account to any AI agent to automate your professional marketing analytics and reporting. This MCP server enables your agent to list ad accounts, monitor campaign performance (impressions, clicks, spend), and retrieve conversion data directly from natural language interfaces using the latest LinkedIn REST API version.
LangChain's ecosystem of 500+ components combines seamlessly with LinkedIn Ads through native MCP adapters. Connect 6 tools via the 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
- Account Discovery — List all accessible ad accounts and retrieve their current status and IDs
- Campaign Monitoring — Query campaign groups and individual campaigns to track your marketing objectives
- Performance Querying — Retrieve real-time performance metrics like impressions, clicks, and cost across various pivots (Account, Campaign, Creative)
- Creative Oversight — List and inspect individual ad variations and their technical configurations
- Conversion Tracking — Retrieve definitions for conversion rules to monitor your return on ad spend (ROAS)
The LinkedIn Ads MCP Server exposes 6 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 LinkedIn Ads to LangChain via MCP
Follow these steps to integrate the LinkedIn Ads 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 6 tools from LinkedIn Ads via MCP
Why Use LangChain with the LinkedIn Ads MCP Server
LangChain provides unique advantages when paired with LinkedIn Ads through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine LinkedIn Ads 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 LinkedIn Ads queries for multi-turn workflows
LinkedIn Ads + LangChain Use Cases
Practical scenarios where LangChain combined with the LinkedIn Ads MCP Server delivers measurable value.
RAG with live data: combine LinkedIn Ads tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query LinkedIn Ads, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain LinkedIn Ads tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every LinkedIn Ads tool call, measure latency, and optimize your agent's performance
LinkedIn Ads MCP Tools for LangChain (6)
These 6 tools become available when you connect LinkedIn Ads to LangChain via MCP:
get_ad_analytics
Requires pivot and dateRange parameters. Query performance metrics (impressions, clicks, spend)
list_ad_accounts
List all accessible LinkedIn Ad Accounts
list_ad_campaigns
List all campaigns for an ad account
list_ad_creatives
List all ad creatives for an ad account
list_campaign_groups
List campaign groups for an ad account
list_conversion_rules
List conversion tracking rules for an account
Example Prompts for LinkedIn Ads in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with LinkedIn Ads immediately.
"List all my LinkedIn Ad accounts."
"Show performance metrics for account ID '500123' for the year 2024."
"List all campaigns associated with my account."
Troubleshooting LinkedIn Ads MCP Server with LangChain
Common issues when connecting LinkedIn Ads to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersLinkedIn Ads + LangChain FAQ
Common questions about integrating LinkedIn Ads 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 LinkedIn Ads 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.
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 LinkedIn Ads to LangChain
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.