How to Use the CaptivateIQ MCP in Pydantic AI

Run type-safe commission audits with Pydantic AI, validating every CaptivateIQ record against your schema at runtime.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect CaptivateIQ MCP to Pydantic AI

Create your Vinkius account to connect CaptivateIQ 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.

GDPR Free for Subscribers

Setup CaptivateIQ with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Enforce strict schemas on CaptivateIQ data

This MCP Server exposes tools like `get_employee_details` to return structured compensation profiles to your Pydantic AI agent. The framework validates the API response against your Pydantic models, guaranteeing that fields like employee IDs and plan codes are formatted correctly. If a record contains unexpected null values, the agent fails immediately instead of processing bad data. This prevents your backend systems from writing broken commission details to your downstream payroll databases.

Validate complex workbook structures with Pydantic AI

The `list_workbooks` tool retrieves your calculation files, while `list_worksheets` gets the individual sheets. Your type-safe agent parses these structures, ensuring every formula and range matches your strict internal validation models. This MCP Server architecture guarantees that your agent never acts on corrupted or incomplete spreadsheet data. You get clean, validated outputs that your finance team can trust without manual double-checking.

Audit payouts and inquiry statuses safely

The `list_commission_payouts` tool lets your agent pull historical payouts, while `list_commission_inquiries` fetches active disputes. Pydantic AI parses these responses into typed Python objects, ensuring status fields like 'approved' or 'pending' match your enums. You can safely run automated reconciliation scripts over the MCP connection without worrying about silent failures. The agent checks the status of your integration via `get_account_status`, ensuring the pipeline is healthy before starting the audit.

Setup guide

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

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

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by CaptivateIQ. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect CaptivateIQ now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about CaptivateIQ MCP in Pydantic AI

Use the MCPToolset class pointing to your Vinkius HTTP endpoint. Pass this toolset directly into your Agent constructor's toolsets parameter to expose tools like `list_employees`.

The framework uses Pydantic to validate parameters before calling tools like `get_employee_details`. If your agent tries to pass an invalid ID format, the call is blocked before hitting the API.

It runs on Vinkius's zero-trust infrastructure, which processes your commission payouts and employee plan structures ephemerally. No payroll data is stored, and your API tokens are kept inside secure environment variables.

Yes, the framework supports both Streamable HTTP and SSE transports. You can configure your connection to match your external hosting requirements on Vinkius.

The framework will raise a ValidationError immediately when parsing `list_payout_statements`. This strict behavior protects your production database from consuming unexpected payload changes.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript