How to Use the Appcues MCP in Pydantic AI

Q: How do I add the Appcues MCP to a Pydantic AI agent?

Install pydantic-ai-slimmcp and use the unified MCPToolset class pointing to your Vinkius HTTP URL. Assign this toolset to your agent's configuration to enable strict schema validation.

Connect the Appcues MCP Server to Pydantic AI for type-safe control over your onboarding flows, user segments, and in-app checklists.

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Connect Appcues MCP to Pydantic AI

Create your Vinkius account to connect Appcues 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 Appcues with Pydantic AI

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Type-Safe Appcues MCP Server Integration

Fetching onboarding data triggers strict runtime validation when your agent calls `get_flow` or `get_segment`. If the API returns a malformed segment rule or a missing flow ID, Pydantic AI catches the error immediately instead of passing garbage data to your model. This strictness saves you from hallucinated parameters during execution. When the agent attempts to run `publish_flow` or `unpublish_flow`, the framework guarantees the payload matches the exact schema required to prevent broken user experiences.

Read User Profiles with High Confidence

Extracting customer details via `get_user` yields predictable, strongly-typed Python objects. You can build complex logic around these profiles knowing that every single attribute is verified before your code ever touches it. Account tiers and completed tutorial states arrive exactly as expected. Bulk data retrieval follows the same rigorous rules. Pulling `list_flows` or `list_segments` returns validated arrays, meaning your agent can iterate through hundreds of onboarding configurations without hitting unexpected null values midway through the loop.

Manage Checklists and System Health

Auditing your available guides starts with the `list_checklists` tool. The model pulls the active directory of checklists, letting you programmatically review which steps your customers are supposed to complete during their initial setup. Destructive actions and connectivity checks demand precision. Your agent verifies the connection with `check_appcues_status` before executing `delete_user`, ensuring that irreversible profile deletions only happen when the system state is perfectly confirmed.

Setup guide

Set up Appcues 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": {
        "appcues-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Appcues MCP in Pydantic AI

Install pydantic-ai-slim[mcp] and use the unified MCPToolset class pointing to your Vinkius HTTP URL. Assign this toolset to your agent's configuration to enable strict schema validation.

Yes, and that is exactly what you want. If a response from get_user no longer matches the expected Pydantic model, the framework fails loudly rather than silently corrupting your downstream logic.

The framework is completely model-agnostic. You can route your onboarding data through OpenAI, Anthropic, or a local Llama instance, and the runtime validation will work identically across all of them.

That specific class is deprecated in newer versions of the framework. You need to switch to the unified MCPToolset approach to connect your external server properly.

We process your customer profiles and checklist states inside an ephemeral V8 sandbox. Since Vinkius handles the authentication layer externally, your raw API keys never touch your agent's runtime memory.

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