How to Use the Salesforce Marketing Cloud MCP in CrewAI

Q: How do I configure CrewAI to use Salesforce Marketing Cloud?

Install crewaitools via pip. You pass the server URL directly into your agent's mcps array. For tighter control, use MCPServerHTTP and apply a tool filter to limit which agent gets which capabilities.

Q: Can CrewAI agents create Salesforce Marketing Cloud campaigns autonomously?

Yes. You assign the sfcreatecampaign tool to an execution agent. It determines the campaign type, sets the start and end dates, and posts the payload to the API based on instructions from the rest of the crew.

Deploy autonomous multi-agent teams to manage your Salesforce Marketing Cloud campaigns with CrewAI.

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Connect Salesforce Marketing Cloud MCP to CrewAI

Create your Vinkius account to connect Salesforce Marketing Cloud to CrewAI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Salesforce Marketing Cloud with CrewAI

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Delegate ROI analysis to a specialized MCP Server agent

`sf_campaign_performance` gives your analyst agent total access to aggregate marketing metrics. The agent pulls campaign counts, total conversions, and actual costs across all channels. It evaluates the performance without waiting for a human to build a dashboard. A secondary researcher agent takes those findings and runs `sf_search_campaigns`. It hunts down specific initiatives by name to see exactly which email blast or webinar drove the numbers. The crew shares this context in memory, building a complete picture of your budget allocation.

Run autonomous campaign management

`sf_create_campaign` allows your execution agent to launch new marketing efforts based on the analyst's recommendations. The agent drafts the initiative, assigns the "Planned" status, and allocates the budgeted cost. Once the campaign exists, a different agent steps in. It uses `sf_add_campaign_member` to enroll specific contacts or leads. The agent assigns initial statuses like "Sent" or "Registered" based on the rules you defined in your CrewAI setup.

Monitor active marketing channels continuously

`sf_campaigns_by_status` feeds your monitoring agent a constant stream of "In Progress" campaigns. The agent tracks lead counts and conversion data to spot anomalies in your active marketing efforts. When the monitor spots a stalled campaign, it flags a moderator agent. That agent executes `sf_campaign_members` to pull the exact list of enrolled leads. It checks their response dates and statuses, determining exactly where the engagement pipeline broke down.

Setup guide

Set up Salesforce Marketing Cloud 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 Salesforce Marketing Cloud tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Salesforce Marketing Cloud 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="Salesforce Marketing Cloud Analyst",
        goal="Access and analyze Salesforce Marketing Cloud data via MCP.",
        backstory="Expert analyst with direct Salesforce Marketing Cloud access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Salesforce Marketing Cloud 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 Salesforce. 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 Salesforce Marketing Cloud MCP in CrewAI

Install `crewai[tools]` via pip. You pass the server URL directly into your agent's `mcps` array. For tighter control, use `MCPServerHTTP` and apply a tool filter to limit which agent gets which capabilities.

Yes. You assign the `sf_create_campaign` tool to an execution agent. It determines the campaign type, sets the start and end dates, and posts the payload to the API based on instructions from the rest of the crew.

The framework maintains shared memory across the crew. An analyst pulls data via `sf_campaign_performance`, and the execution agent reads those exact conversion metrics from memory before deciding where to add new campaign members.

The framework supports stdio, SSE, and Streamable HTTP. You choose the transport layer that fits your deployment environment when configuring the agent's connection array.

This MCP Server reads actual costs, budget allocations, and individual lead response data. The Vinkius platform isolates this activity using strict ephemeral sandboxing. The execution context vanishes the moment your Python script terminates the connection.

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