How to Use the Salesforce Marketing Cloud MCP in Pydantic AI

Q: How does the Pydantic AI agent handle the strict requirements of campaign member creation?

The tool sfaddcampaignmember requires either a contactId or a leadId. The Pydantic AI schema enforces this mutual exclusivity at the Python level, stopping the agent from passing both and triggering a Salesforce Marketing Cloud API error.

Run type-safe Python agents with Pydantic AI to manage Salesforce Marketing Cloud campaigns without silent failures using this MCP Server.

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Connect Salesforce Marketing Cloud MCP to Pydantic AI

Create your Vinkius account to connect Salesforce Marketing Cloud to Pydantic AI — 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 Pydantic AI

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Validate Campaigns at Runtime with Pydantic AI

`sf_create_campaign` enforces strict parameter typing when inserting new initiatives into Salesforce Marketing Cloud. The Pydantic AI framework validates the campaign name, type, and budgeted cost against strict schemas before sending the payload. If your Pydantic AI agent attempts to write an invalid campaign type, the runtime raises a validation error immediately. This prevents malformed data from hitting your Salesforce Marketing Cloud instance, ensuring your live database remains clean and structured.

Enforce Member Schema Validation

`sf_add_campaign_member` adds a lead or contact to an active Salesforce Marketing Cloud campaign while ensuring you only provide one identifier. The tool's strict schema requires either a contactId or a leadId, which Pydantic AI validates at the boundary. To verify enrollment, the agent queries `sf_campaign_members` to return typed Salesforce Marketing Cloud member lists. The Pydantic AI framework parses the returned statuses and response dates into Python models, guaranteeing your agent never processes corrupt null values.

Type-Safe Performance Audits via MCP Server

`sf_campaign_performance` returns structured Salesforce Marketing Cloud metrics, grouping lead counts, conversions, and costs by campaign type. The Pydantic AI framework parses this payload into validated Python objects, making it safe for financial calculations. Your Pydantic AI agent can then use `sf_campaigns_by_status` to filter active versus completed Salesforce Marketing Cloud initiatives. Because the response matches a strict schema, your agent can calculate cost-per-lead metrics without risking runtime type crashes.

Setup guide

Set up Salesforce Marketing Cloud MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "salesforce-marketing-cloud-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Salesforce Marketing Cloud tools.",
)

result = await agent.run("List recent Salesforce Marketing Cloud transactions")
print(result.output)

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Common questions about Salesforce Marketing Cloud MCP in Pydantic AI

Install the slim package with the mcp extra, then create an `MCPToolset` pointing to your HTTP or SSE endpoint. Pass this toolset into your Pydantic AI `Agent` constructor to give your agent access to the campaign tools.

Pydantic AI will raise a validation error at the boundary. If a tool like `sf_campaign_performance` returns a string where an integer is expected, the framework fails loudly instead of letting your agent make decisions on corrupted data.

The tool `sf_add_campaign_member` requires either a contactId or a leadId. The Pydantic AI schema enforces this mutual exclusivity at the Python level, stopping the agent from passing both and triggering a Salesforce Marketing Cloud API error.

Yes, Pydantic AI is model-agnostic. You can run these campaign tools using local models or commercial APIs, as long as the model supports tool calling and the MCP server is running.

The server only processes campaign configurations, member IDs, and budget metrics. Vinkius manages the authentication tokens in an ephemeral, zero-trust V8 sandbox, ensuring your Salesforce Marketing Cloud credentials are never exposed to the Pydantic AI execution environment.

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