How to Use the Fomo MCP in Pydantic AI

Q: How do I configure Fomo with Pydantic AI?

Install pydantic-ai-slimmcp. Initialize the external server using the unified MCPToolset class with your HTTP endpoint, and pass it into the toolsets array of your Agent constructor.

Q: What happens if the Fomo MCP Server returns unexpected data to Pydantic AI?

The framework immediately throws a validation error. If getevent returns a missing field, Pydantic AI catches it before your agent can act on the malformed social proof data.

Q: Can my Pydantic AI agent create new Fomo templates?

It calls createtemplate using the parameters defined in your schema. Pydantic AI forces the LLM to provide the correct variable types before the API request ever fires.

Type-safe Fomo event execution for your Pydantic AI agents.

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Create your Vinkius account to connect Fomo to Pydantic AI 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 Fomo with Pydantic AI

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Validated Event Execution

The `create_event` MCP tool requires precise payload structures, and Pydantic AI ensures your agent never sends hallucinated fields to the Fomo API. Before the agent pushes a new purchase notification, the framework validates the data against your exact Pydantic models. If the agent generates a bad timestamp, it fails loudly. You get the same strict validation when modifying live notifications with `update_event` or scrubbing data with `delete_event`. You don't have to worry about silent corruption in your social proof feed. The agent either executes the operation perfectly or throws a validation error immediately.

Type-Safe Template Syncing

Your agent fetches active designs using `list_templates` and `get_template`, parsing the API response directly into typed Python objects. When the agent uses `create_template` to deploy a new notification format, Pydantic AI guarantees the variables match the required Fomo schema. This strict adherence to types makes building e-commerce automations highly predictable. You pass the Fomo MCP Server into your agent via `MCPToolset("http://...")`, and the framework handles the auto-discovery while enforcing runtime checks on every tool call.

Fomo MCP Server Auditing

You can build an auditing agent that pulls your account setup via `get_application_info` and checks active data sources using `list_integrations`. Pydantic AI validates the response from Fomo, ensuring your agent always works with structurally sound data. When tracking broadcast history with `list_push_messages` or checking recent activity via `list_events`, the framework guarantees the output matches your expected schema. Whether you use local models or Anthropic, the type safety remains identical across the entire integration.

Setup guide

Set up Fomo 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": {
        "fomo-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Fomo transactions")
print(result.output)

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Common questions about Fomo MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Initialize the external server using the unified `MCPToolset` class with your HTTP endpoint, and pass it into the `toolsets` array of your Agent constructor.

The framework immediately throws a validation error. If `get_event` returns a missing field, Pydantic AI catches it before your agent can act on the malformed social proof data.

It calls `create_template` using the parameters defined in your schema. Pydantic AI forces the LLM to provide the correct variable types before the API request ever fires.

Pydantic AI supports Streamable HTTP and SSE transports for MCP servers. You can stream the tool execution process as the agent evaluates `list_events` and decides to trigger new notifications.

This server processes live purchase notifications and push message payloads. Authentication requires a single endpoint token, and the ephemeral architecture ensures no customer event data persists in the integration layer after Pydantic AI validates the response.

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