SmartHR MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add SmartHR 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 SmartHR. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in SmartHR?"
)
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 SmartHR MCP Server
Connect your SmartHR directory to any AI agent and empower your team to query employee data, organizational hierarchies, and payroll records securely. Interact with your organization's human capital database through natural language without ever switching tabs.
LlamaIndex agents combine SmartHR 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
- Employee Tracking — Call the
list_crewstool to retrieve the roster of all employees and cross-reference demographic metadata - Department Hierarchies — Ask your AI to
list_departmentsor checklist_positionsto understand the internal structure - Deep Employee Profiles — Perform targeted queries using
get_crew_detailsorlist_crew_dependentsto fetch highly sensitive contract details - Payroll Overviews — Securely query active or historical payroll ledgers to audit compensation scaling
The SmartHR 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 SmartHR to LlamaIndex via MCP
Follow these steps to integrate the SmartHR 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 SmartHR
Why Use LlamaIndex with the SmartHR MCP Server
LlamaIndex provides unique advantages when paired with SmartHR through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine SmartHR tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain SmartHR tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query SmartHR, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what SmartHR tools were called, what data was returned, and how it influenced the final answer
SmartHR + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the SmartHR MCP Server delivers measurable value.
Hybrid search: combine SmartHR real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query SmartHR 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 SmartHR for fresh data
Analytical workflows: chain SmartHR queries with LlamaIndex's data connectors to build multi-source analytical reports
SmartHR MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect SmartHR to LlamaIndex via MCP:
get_crew_details
Retrieves details for a specific employee
list_crew_dependents
Lists dependents for a specific employee
list_crews
Lists all employees (crews) in SmartHR
list_departments
Lists all organizational departments
list_employment_types
Lists all employment types
list_establishments
Lists business establishments or office locations
list_payrolls
Lists employee payroll records
list_positions
Lists all job positions or roles
Example Prompts for SmartHR in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with SmartHR immediately.
"Fetch the entire roster and list the top 3 job positions that occur most frequently."
"Retrieve the details for crew member `crew-8f192`, including their enrolled dependents."
"List all physical establishments the company operates."
Troubleshooting SmartHR MCP Server with LlamaIndex
Common issues when connecting SmartHR to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpSmartHR + LlamaIndex FAQ
Common questions about integrating SmartHR 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 SmartHR 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 SmartHR to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.