Hurma MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Create Candidate, Create Leave Request, Export Overtimes, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Hurma as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The Hurma app connector for LlamaIndex is a standout in the Productivity category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Hurma. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Hurma?"
)
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 Hurma MCP Server
Connect your Hurma instance to any AI agent and manage your HR operations through natural conversation.
LlamaIndex agents combine Hurma tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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
- Recruiting Pipeline — List all candidates, inspect profiles, create new candidate records, and track hiring progress
- Employee Directory — Browse all employees with department and position details
- Time-Off Management — Monitor out-of-office schedules and leave requests
- Department Structure — Browse organizational departments
- Position Management — List all job positions
- Onboarding Tracking — Monitor new hire checklists and progress
The Hurma MCP Server exposes 12 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.
All 12 Hurma tools available for LlamaIndex
When LlamaIndex connects to Hurma through Vinkius, your AI agent gets direct access to every tool listed below — spanning employee-directory, time-off-tracking, onboarding, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Create a new candidate
Create a new leave or absence request
Export overtime data
Get details for a specific candidate
Get details for a specific employee
Get employee vacation balance
List recruitment candidates
List custom field definitions
List all company departments
List all employees
List employees currently out of office
List recruitment stages
Connect Hurma to LlamaIndex via MCP
Follow these steps to wire Hurma into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Hurma MCP Server
LlamaIndex provides unique advantages when paired with Hurma through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Hurma tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Hurma tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Hurma, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Hurma tools were called, what data was returned, and how it influenced the final answer
Hurma + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Hurma MCP Server delivers measurable value.
Hybrid search: combine Hurma real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Hurma 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 Hurma for fresh data
Analytical workflows: chain Hurma queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Hurma in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Hurma immediately.
"Show all candidates in the pipeline and employees out of office this week."
"List all employees in Engineering and create a new candidate for Senior Backend."
"Show onboarding status for new hires and all departments."
Troubleshooting Hurma MCP Server with LlamaIndex
Common issues when connecting Hurma to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpHurma + LlamaIndex FAQ
Common questions about integrating Hurma MCP Server with LlamaIndex.