Can I check the total commission for a specific employee using the agent?

Yes! Use the `get_employee_details` tool or the `list_payout_statements` tool. Your agent will fetch the relevant data, including the total commission amount for the period.

How do I list all the processed payouts for my team?

Simply ask the agent to `list_commission_payouts`. It will retrieve the full history of processed payouts from your CaptivateIQ account, including dates and totals.

Does the integration allow creating a new payout manual adjustment?

The current toolset focuses on querying and monitoring (Read-Only) for data analysis and payroll extraction. Adjustments or changes to the calculation logic should be managed via the CaptivateIQ web platform.

CaptivateIQ MCP Server for LlamaIndex 8 tools — connect in under 2 minutes

Name: CaptivateIQ
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 8 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add CaptivateIQ as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to CaptivateIQ. "
            "You have 8 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in CaptivateIQ?"
    )
    print(response)

asyncio.run(main())

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About CaptivateIQ MCP Server

Connect your CaptivateIQ account to any AI agent and orchestrate your incentive compensation, commission tracking, and payroll integration workflows through natural conversation.

LlamaIndex agents combine CaptivateIQ tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Payout Oversight — List and retrieve detailed metadata for all processed commission payouts across your organization.
Statement Management — Access individualized payout statements for employees to verify earnings and calculations.
Employee Coordination — List and retrieve detailed profiles for all employees, including their plan designations and hierarchy.
Workbook Monitoring — Access and list your calculation workbooks and worksheets to ensure transparency in your commission logic.
Dispute Tracking — Monitor and list commission inquiries or disputes directly from your workspace.
Financial Reporting — Retrieve core account and integration information straight from your workspace.

The CaptivateIQ MCP Server exposes 8 tools through the Vinkius. Connect it to LlamaIndex 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 CaptivateIQ to LlamaIndex via MCP

Follow these steps to integrate the CaptivateIQ MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

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 8 tools from CaptivateIQ

Why Use LlamaIndex with the CaptivateIQ MCP Server

LlamaIndex provides unique advantages when paired with CaptivateIQ through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine CaptivateIQ tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain CaptivateIQ tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query CaptivateIQ, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what CaptivateIQ tools were called, what data was returned, and how it influenced the final answer

CaptivateIQ + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the CaptivateIQ MCP Server delivers measurable value.

Hybrid search: combine CaptivateIQ real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query CaptivateIQ to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying CaptivateIQ for fresh data

Analytical workflows: chain CaptivateIQ queries with LlamaIndex's data connectors to build multi-source analytical reports

CaptivateIQ MCP Tools for LlamaIndex (8)

These 8 tools become available when you connect CaptivateIQ to LlamaIndex via MCP:

get_account_status

Retrieve core account/integration information

get_employee_details

Get details of a specific employee

list_commission_inquiries

List commission disputes and inquiries (generic search)

list_commission_payouts

List all processed commission payouts

list_employees

List all employee records and plan designations

list_payout_statements

List individualized payout statements for employees

list_workbooks

List all calculation workbooks

list_worksheets

List all calculation worksheets

Example Prompts for CaptivateIQ in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with CaptivateIQ immediately.

"List all processed commission payouts in CaptivateIQ."

"Show the payout statement for employee John Doe."

"Which calculation workbooks are available in my account?"

Troubleshooting CaptivateIQ MCP Server with LlamaIndex

Common issues when connecting CaptivateIQ to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

CaptivateIQ + LlamaIndex FAQ

Common questions about integrating CaptivateIQ MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query CaptivateIQ tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.