Argyle MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Argyle as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
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 Argyle. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Argyle?"
)
print(response)
asyncio.run(main())
* 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 Argyle MCP Server
The Argyle MCP Server brings automated employment and income verification directly to your AI agent. Seamlessly manage your user verification workflows, retrieve detailed employment history, and monitor income totals using simple natural language.
LlamaIndex agents combine Argyle tool responses with indexed documents for comprehensive, grounded answers. Connect 7 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.
Key Capabilities
- User Management — List all users in your Argyle account and create new unique user IDs for verification flows.
- Employment Verification — Retrieve verified employment status, hire dates, job titles, and employer details from the source.
- Income Analysis — Access detailed income totals and breakdown, including YTD, monthly, and per-pay-period data.
- Payout Tracking — List individual pay period details (payouts) to understand gross/net pay and deductions.
- Verified Identities — Retrieve verified name, address, and contact information directly from payroll sources.
- Secure Data Access — Uses secure API keys and supports sandbox mode for safe testing and production usage.
The Argyle MCP Server exposes 7 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 Argyle to LlamaIndex via MCP
Follow these steps to integrate the Argyle 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 7 tools from Argyle
Why Use LlamaIndex with the Argyle MCP Server
LlamaIndex provides unique advantages when paired with Argyle through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Argyle tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Argyle tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Argyle, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Argyle tools were called, what data was returned, and how it influenced the final answer
Argyle + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Argyle MCP Server delivers measurable value.
Hybrid search: combine Argyle real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Argyle 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 Argyle for fresh data
Analytical workflows: chain Argyle queries with LlamaIndex's data connectors to build multi-source analytical reports
Argyle MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Argyle to LlamaIndex via MCP:
create_user
Create a new user in Argyle
get_account_check
Verify Argyle account connection
get_employment
Retrieve employment history for a specific user
get_income
Retrieve income totals and breakdown for a user
list_identities
Retrieve verified identity information for a user
list_payouts
List individual pay period details (payouts) for a user
list_users
List all users created in your Argyle account
Example Prompts for Argyle in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Argyle immediately.
"List all users in my Argyle account."
"Show me the employment history for user 'user_12345'."
"What is the total YTD income for user 'user_abc'?"
Troubleshooting Argyle MCP Server with LlamaIndex
Common issues when connecting Argyle to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpArgyle + LlamaIndex FAQ
Common questions about integrating Argyle MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Argyle with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Argyle to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.