How to Use the Adobe Analytics MCP in CrewAI

Q: How do I connect CrewAI to Adobe Analytics?

Install crewaitools via pip. Drop your Vinkius endpoint URL directly into the mcps parameter of your Agent definition. The framework handles the MCP connection automatically.

Q: How do I restrict certain tools to specific agents?

Import MCPServerHTTP from crewai.mcp. Use the toolfilter parameter to expose only the specific operations that agent needs.

Deploy a squad of CrewAI agents to autonomously monitor and analyze your Adobe Analytics data.

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Connect Adobe Analytics MCP to CrewAI

Create your Vinkius account to connect Adobe Analytics 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.

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Setup Adobe Analytics with CrewAI

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Autonomous Reporting Squads

A researcher agent uses `get_report` to extract raw performance data while an analyst agent interprets the JSON output. CrewAI passes the context between them using shared memory after hitting the MCP endpoint. You build an entire data team out of Python scripts that run without human supervision. Setting this up requires passing the Vinkius URL into the `mcps` array of your agent. The agents figure out the timing themselves. Researchers pull the numbers, and the analyst writes the executive summary.

Map Metrics with CrewAI Agents

Your setup agent runs `get_dimensions` and `get_metrics` to understand the exact shape of your data before asking for reports. It stores this schema in the crew's memory bank. Other agents reference this map to ensure they request valid variables. Hierarchical execution shines here. A manager agent reviews the available metrics and delegates specific queries to worker agents. Workers execute the queries in parallel against the MCP Server.

Cross-Reference Target Segments

Invoking `list_segments` lets a dedicated audience agent pull all active definitions. It compares these segments against the available report suites found via `list_report_suites`. Crews identify gaps in your tracking strategy and flag them automatically. Advanced setups use `MCPServerHTTP` to filter which agents get which tools. You give the researcher access to the heavy reports, but restrict the auditor agent to just scanning the segment lists.

Setup guide

Set up Adobe Analytics MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. 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 Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Adobe Analytics tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Adobe Analytics Analyst",
    goal="Access and analyze Adobe Analytics data via MCP.",
    backstory="Expert analyst with direct Adobe Analytics access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Adobe Analytics transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

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="Adobe Analytics Analyst",
        goal="Access and analyze Adobe Analytics data via MCP.",
        backstory="Expert analyst with direct Adobe Analytics access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Adobe Analytics transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Adobe Analytics. 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.

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Common questions about Adobe Analytics MCP in CrewAI

Install `crewai[tools]` via pip. Drop your Vinkius endpoint URL directly into the `mcps` parameter of your Agent definition. The framework handles the MCP connection automatically.

Yes. Assigning the MCP Server to multiple agents works perfectly. They coordinate their API calls based on the execution strategy you define.

Import `MCPServerHTTP` from `crewai.mcp`. Use the `tool_filter` parameter to expose only the specific operations that agent needs.

Frameworks use shared memory to store previous results. If one agent pulls a massive JSON payload, the other agents can reference that data without hitting the API again.

Security is handled at the transport layer. Vinkius wraps the execution in a hardened V8 isolate. Proprietary traffic data streams directly to your Python environment and leaves zero footprint on our servers.

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