How to Use the CaptivateIQ MCP in LlamaIndex

Q: Can I search the text of CaptivateIQ payout statements with LlamaIndex?

Yes. Your agent can use the listpayoutstatements tool and feed the contents into a LlamaIndex data structure. This makes the text of the statements themselves searchable for specific details or amounts.

Q: What kind of setup is needed for this MCP Server?

After installing the adapter, you use McpToolSpec to load the CaptivateIQ tools. You then pass that tool list to your agent, which can start calling tools and indexing their output.

Turn your CaptivateIQ data into a queryable knowledge base using LlamaIndex.

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 LlamaIndex

Create your Vinkius account to connect CaptivateIQ to LlamaIndex 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 LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build a searchable payout history

Your LlamaIndex agent can periodically run `list_commission_payouts` and `list_payout_statements`. The output from these tools is automatically indexed into a vector store of your choice. This builds a persistent, searchable history of all commission activity. When a user asks, "Show me all of John's payouts from last quarter," the agent queries the index. It gets an instant, grounded answer without having to hit the live API again.

Answer questions about comp plans

Use the `list_employees` and `get_employee_details` tools to populate an index with your team's compensation plan assignments. This creates a snapshot of your org structure inside a knowledge base that your agent can query. Your agent can now answer natural language questions like "Who is on the Enterprise Sales plan?" by performing a semantic search over that indexed data. It's faster and more efficient than making repeated API calls for information that doesn't change often.

Ground your LlamaIndex agent in facts

When a commission question comes up, use Retrieval-Augmented Generation (RAG). Your agent can fetch relevant context from the indexed results of `list_commission_inquiries` and `list_worksheets`. This gives it the factual data it needs to explain why a commission was calculated a certain way. This MCP Server provides the live data, and LlamaIndex turns it into institutional knowledge. Your agent's answers get grounded in actual workbook logic and payout records, preventing hallucinations.

Setup guide

Set up CaptivateIQ MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all CaptivateIQ MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to CaptivateIQ tools.",
)
response = await agent.run("List recent CaptivateIQ data")

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 LlamaIndex

You build a RAG pipeline. Have your agent periodically run tools like `list_commission_payouts`, index the results, and then ask it questions about trends, outliers, or totals over time.

Yes. Your agent can use the `list_payout_statements` tool and feed the contents into a LlamaIndex data structure. This makes the text of the statements themselves searchable for specific details or amounts.

LlamaIndex creates a searchable knowledge base from your CaptivateIQ data. This lets you ask complex questions about historical data without constantly hitting the live API, and it grounds your agent's answers in facts.

After installing the adapter, you use `McpToolSpec` to load the CaptivateIQ tools. You then pass that tool list to your agent, which can start calling tools and indexing their output.

The connection is point-to-point. Your commission and employee data is fetched by the agent through a secure Vinkius endpoint and passed directly to your LlamaIndex application. Nothing is logged or stored on our platform.

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