How to Use the Certify (Emburse) MCP in Pydantic AI

Validate Certify (Emburse) expense data at runtime with type-safe Pydantic AI agents.

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Connect Certify (Emburse) MCP to Pydantic AI

Create your Vinkius account to connect Certify (Emburse) 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 Certify (Emburse) with Pydantic AI

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Validate GL dimensions in Pydantic AI

The `get_gl_dimensions` tool fetches your accounting categories and validates them against runtime schemas before your agent can use them. This prevents corrupted accounting codes from breaking your downstream databases. You initialize this connection using MCPToolset with your Vinkius HTTP endpoint. If the server returns a malformed dimension, your agent raises a validation error immediately.

Parse user lists with this MCP Server

This MCP Server exposes `list_certify_users` to verify employee profiles against strict Pydantic models. The agent uses this clean data to confirm which team members are authorized to file reports. This setup avoids silent failures. You get guaranteed type safety across your entire user list, preventing your LLM from hallucinating employee IDs.

Verify invoices and receipts safely

The `list_certify_receipts` tool works with `list_certify_invoices` to pull financial records directly into your typed code. The agent checks that every receipt matches an active invoice without type coercion issues. Both SSE and Streamable HTTP transports are supported. This ensures reliable streaming of large invoice batches without dropping connections or corrupting the payload.

Setup guide

Set up Certify (Emburse) 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": {
        "certify-emburse-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Certify (Emburse) MCP in Pydantic AI

Install the mcp extra and pass the server URL to the MCPToolset constructor. Then add that toolset to your Agent instance to register the tools.

The framework will raise a validation error instantly. This prevents your agent from acting on corrupted expense reports or bad GL dimensions.

No, because the MCP integration automatically translates the server schemas into Pydantic models. This ensures your code remains type-safe without writing boilerplate schemas.

Absolutely, because this toolset is completely model-agnostic in Pydantic AI. You can use it to validate Certify (Emburse) data using local models or commercial APIs.

Data is fetched via an encrypted SSE or HTTP stream directly to your validated runtime. The Vinkius sandbox ensures your financial documents are never cached or exposed to external networks.

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