How to Use the Argyle MCP in LlamaIndex

Q: Does Argyle work with LlamaIndex filters?

You can use allowedtools filters to restrict the agent's access. This ensures LlamaIndex only interacts with the specific payroll data you authorize.

Index your Argyle payroll data into LlamaIndex to query employment history semantically.

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Connect Argyle MCP to LlamaIndex

Create your Vinkius account to connect Argyle 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.

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Setup Argyle with LlamaIndex

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Searchable payroll history in LlamaIndex

Pipe the results of `list_payouts` directly into your vector store. Your index now understands pay frequency and individual deposit amounts for your users. LlamaIndex turns these API calls into queryable context. You can ask questions about historical earnings across your entire user base.

Verify identities with vector context

Run `list_identities` to ground your search in real-world documentation. The agent pulls verified identity details into the knowledge base for comparison against user-provided forms. This MCP Server keeps your index current. You query the latest verified data points instead of relying on stale documents.

Monitor account connections

Use `get_account_check` to see if a user has a valid link. LlamaIndex stores this connectivity status as metadata, helping your RAG system prioritize active accounts. You query the index to find users who need re-authentication. It keeps your data pipeline focused on healthy connections.

Setup guide

Set up Argyle 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 Argyle 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 Argyle tools.",
)
response = await agent.run("List recent Argyle data")

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Common questions about Argyle MCP in LlamaIndex

Tool outputs become part of your searchable index. LlamaIndex converts the structured JSON from payroll providers into vector embeddings for accurate retrieval.

Absolutely. By passing tools to your agent, you allow it to fetch live payroll data and combine it with your existing documents for grounded answers.

You can use allowed_tools filters to restrict the agent's access. This ensures LlamaIndex only interacts with the specific payroll data you authorize.

Trigger a refresh of your index after calling the server tools. The updated employment data is then embedded and ready for your next query.

We secure the connection via an endpoint token. Your employment history and income totals are never persisted by us, ensuring your private payroll data stays under your control.

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