Can I view recent transactions across the company?

Yes! Use the `list_transactions` tool to retrieve recent corporate card usage and merchant details.

How do I check my available budget?

Simply ask the agent to `list_budgets` to retrieve budget limits and current spend amounts.

Does the integration allow me to create virtual cards?

Currently, the toolset focuses on querying and auditing (listing cards, budgets, transactions). Creating cards must be done on the platform.

BILL Spend & Expense MCP Server for Pydantic AI 5 tools — connect in under 2 minutes

Name: BILL Spend & Expense
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 5 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect BILL Spend & Expense through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to BILL Spend & Expense "
            "(5 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in BILL Spend & Expense?"
    )
    print(result.data)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About BILL Spend & Expense MCP Server

Connect your BILL Spend & Expense (formerly Divvy) account to any AI agent and orchestrate your corporate spending through natural conversation.

Pydantic AI validates every BILL Spend & Expense tool response against typed schemas, catching data inconsistencies at build time. Connect 5 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Budget Oversight — List and inspect active budgets to track spending against limits.
Card Management — View virtual and physical cards assigned to employees.
Transaction Auditing — Retrieve real-time transactions and merchant data.
User Management — List employees and review their roles.
Reimbursement Tracking — Check the status of out-of-pocket expense claims.

The BILL Spend & Expense MCP Server exposes 5 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect BILL Spend & Expense to Pydantic AI via MCP

Follow these steps to integrate the BILL Spend & Expense MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 5 tools from BILL Spend & Expense with type-safe schemas

Why Use Pydantic AI with the BILL Spend & Expense MCP Server

Pydantic AI provides unique advantages when paired with BILL Spend & Expense through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your BILL Spend & Expense integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your BILL Spend & Expense connection logic from agent behavior for testable, maintainable code

BILL Spend & Expense + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the BILL Spend & Expense MCP Server delivers measurable value.

Type-safe data pipelines: query BILL Spend & Expense with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple BILL Spend & Expense tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query BILL Spend & Expense and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock BILL Spend & Expense responses and write comprehensive agent tests

BILL Spend & Expense MCP Tools for Pydantic AI (5)

These 5 tools become available when you connect BILL Spend & Expense to Pydantic AI via MCP:

list_budgets

List all budgets

list_cards

List all cards

list_reimbursements

List all reimbursements

list_transactions

List recent transactions

list_users

List all users

Example Prompts for BILL Spend & Expense in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with BILL Spend & Expense immediately.

"List all active budgets."

"Show recent transactions."

"List pending reimbursements."

Troubleshooting BILL Spend & Expense MCP Server with Pydantic AI

Common issues when connecting BILL Spend & Expense to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

BILL Spend & Expense + Pydantic AI FAQ

Common questions about integrating BILL Spend & Expense MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your BILL Spend & Expense MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.