How to Use the Adtelligent MCP in CrewAI
Run an autonomous programmatic ad team by connecting CrewAI agents to your Adtelligent marketing tools.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Adtelligent MCP to CrewAI
Create your Vinkius account to connect Adtelligent to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.
Autonomous DSP Campaign Auditing in CrewAI
Deploy a specialized CrewAI team to manage your Adtelligent programmatic inventory. An analyst agent uses `list_campaigns` to pull active DSP setups, while a controller agent evaluates the budget distribution. The agents collaborate in real-time to flag underperforming programmatic assets. This CrewAI multi-agent setup replaces manual advertising spreadsheets. Because the crew shares memory, the controller agent remembers previous campaign statuses while checking new data, preventing duplicate API calls and saving your API quota.
Collaborative SSP Performance Reports
Let your CrewAI agents handle complex Adtelligent programmatic reporting tasks. A data collector agent calls `get_ssp_report` to get raw yield metrics. It then hands this data to a writer agent who formats a clean summary of SSP yield metrics for your Slack channel. You don't have to coordinate the handoff between programmatic agents. CrewAI handles the sequential execution, passing the SSP stats from one agent to the next while maintaining context and authentication headers.
Automated Advertiser Directory Management
Keep your Adtelligent DSP accounts organized across multiple business units using CrewAI. A manager agent coordinates a search using `list_advertisers` to verify active partners. It then directs a researcher agent to look up missing contact info for newly created programmatic accounts. The CrewAI agents work in parallel or hierarchy depending on your setup. They use the MCP Server to pull live account data directly into their shared workspace, ensuring they always make decisions based on real-time facts.
Set up Adtelligent MCP in CrewAI
Prerequisites
- Python 3.10+ installed
-
crewaipackage (pip install crewai) - Active Vinkius subscription with a valid endpoint token
- 1
Install CrewAI
Run
pip install crewaito install the framework. MCP support is built-in via themcpsparameter. - 2
Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the
mcpslist. Replace[YOUR_TOKEN_HERE]with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically. - 3
Kick off your crew
Create a
Crewwith your agent and tasks. Callcrew.kickoff()— the agent will automatically invoke Adtelligent tools as needed.
from crewai import Agent, Task, Crew
agent = Agent(
role="Adtelligent Analyst",
goal="Access and analyze Adtelligent data via MCP.",
backstory="Expert analyst with direct Adtelligent access.",
mcps=[
"https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
],
)
task = Task(
description="List recent Adtelligent transactions",
agent=agent,
expected_output="A summary of recent activity",
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result) Prerequisites
- Python 3.10+ installed
-
crewai+crewai-toolspackages - Active Vinkius subscription with a valid endpoint token
- 1
Install dependencies
Run
pip install crewai crewai-tools. TheMCPServerAdapterhandles lifecycle management and tool conversion. - 2
Connect with MCPServerAdapter
Use
MCPServerAdapteras a context manager withSseServerParameterspointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle. - 3
Assign tools and run
Pass the returned
mcp_toolsto your agent'stoolsparameter. The adapter converts MCP tools to nativeBaseToolobjects compatible with all CrewAI agents.
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters
server_params = SseServerParameters(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
with MCPServerAdapter(server_params) as mcp_tools:
agent = Agent(
role="Adtelligent Analyst",
goal="Access and analyze Adtelligent data via MCP.",
backstory="Expert analyst with direct Adtelligent access.",
tools=mcp_tools,
)
task = Task(
description="List recent Adtelligent transactions",
agent=agent,
expected_output="A summary of recent activity",
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Adtelligent. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
Why Choose Vinkius
Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.
Real-time monitoring
Live
visibility into every interaction
Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.
Built-in savings
60%
lower AI costs
Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.
Single dashboard
One
place for every integration
Every tool your AI connects to, managed from a single screen. One account, complete control.
Common questions about Adtelligent MCP in CrewAI
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