LinkedIn Ads MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add LinkedIn Ads as an MCP tool provider through the 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 LinkedIn Ads. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in LinkedIn Ads?"
)
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 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.
LlamaIndex agents combine LinkedIn Ads tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through the 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
- 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 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 LinkedIn Ads to LlamaIndex via MCP
Follow these steps to integrate the LinkedIn Ads 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 6 tools from LinkedIn Ads
Why Use LlamaIndex with the LinkedIn Ads MCP Server
LlamaIndex provides unique advantages when paired with LinkedIn Ads through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine LinkedIn Ads tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain LinkedIn Ads tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query LinkedIn Ads, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what LinkedIn Ads tools were called, what data was returned, and how it influenced the final answer
LinkedIn Ads + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the LinkedIn Ads MCP Server delivers measurable value.
Hybrid search: combine LinkedIn Ads real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query LinkedIn Ads 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 LinkedIn Ads for fresh data
Analytical workflows: chain LinkedIn Ads queries with LlamaIndex's data connectors to build multi-source analytical reports
LinkedIn Ads MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect LinkedIn Ads to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting LinkedIn Ads to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpLinkedIn Ads + LlamaIndex FAQ
Common questions about integrating LinkedIn Ads 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 LinkedIn Ads with your favorite client
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Connect LinkedIn Ads to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.