How to Use the Elevio MCP in Pydantic AI

Q: How do I find stale drafts in Elevio using a Pydantic AI agent?

Your agent just needs to call the listdraftknowledgearticles tool. The MCP server handles the API call to Elevio, and Pydantic AI ensures the list of articles it returns is correctly structured before your code touches it.

Build reliable agents with Pydantic AI that can safely manage your Elevio knowledge base with validated, type-safe tools.

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Connect Elevio MCP to Pydantic AI

Create your Vinkius account to connect Elevio 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 Elevio with Pydantic AI

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Type-Safe Knowledge Base Audits

Stop writing defensive code for messy API responses. This toolset ensures every call to Elevio is validated. When your agent runs a `quick_content_health_audit`, the response is guaranteed to match the Pydantic model, or it fails with a clear error. This means you can trust the data you get back from tools like `list_draft_knowledge_articles` and `list_empty_knowledge_categories`. Your agent won't get a malformed list and start deleting the wrong things. It's about building predictable automation.

Your Pydantic AI Agent & Elevio

When your agent needs to read an article, correctness is critical. With Pydantic AI, calling `get_article_details` means the result is parsed into a strict model. If the API ever adds a field or changes a data type, your code knows immediately. This allows you to build robust RAG applications. Your agent can `search_knowledge_articles`, get a list of IDs, and then confidently fetch the full content knowing the data structure is exactly what it expects. No more silent failures or corrupted context.

Manage Your MCP Server with Confidence

Your agent can do more than just manage content. It can inspect the Elevio configuration itself. When it calls `list_knowledge_categories` or `list_active_modules`, Pydantic AI validates the structure of the response. This is key for building resilient monitoring systems. You can have an agent that periodically checks `get_elevio_account_metadata`. If the response schema is ever wrong, you get a `ValidationError`, not an agent that misinterprets your API limits.

Setup guide

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

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

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

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Common questions about Elevio MCP in Pydantic AI

Pydantic AI automatically validates the data from every Elevio tool call against a predefined schema. If the API returns something unexpected from `get_article_details`, for example, it raises an error instead of letting your agent process bad data.

Yes. Pydantic AI is model-agnostic. You can use OpenAI, Gemini, Anthropic, or a local model to power the agent that calls the Elevio tools like `search_knowledge_articles`.

Your agent just needs to call the `list_draft_knowledge_articles` tool. The MCP server handles the API call to Elevio, and Pydantic AI ensures the list of articles it returns is correctly structured before your code touches it.

It's straightforward. You install the library, then create an `MCPToolset` instance with the Vinkius URL. Pydantic AI handles the tool discovery and runtime validation for you.

Pydantic AI itself is a validation library; it doesn't store your data. When your agent calls a tool, Elevio's article content and metadata are passed through your application's memory for validation and then discarded. Vinkius provides an ephemeral, secure transport layer.

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