How to Use the WebHR MCP in LlamaIndex

Q: How does LlamaIndex improve WebHR reporting?

It takes tool outputs—like those from listemployees—and indexes them into a vector store. This means you can query past HR data semantically, getting answers grounded in the actual API records.

Ground WebHR knowledge in vector stores with LlamaIndex for robust retrieval.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect WebHR MCP to LlamaIndex

Create your Vinkius account to connect WebHR 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 WebHR with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build RAG Knowledge Bases from WebHR Data

Instead of just calling a tool, you can index the results. For instance, run `list_job_postings` and immediately feed those open roles into an index. Later, your agent can search that knowledge base to find specific details about job requirements or departments. LlamaIndex makes the WebHR output searchable. You combine live API data with documents, so you get answers grounded in actual `list_company_departments` records, not guesses.

Retrieve Employee Data using LlamaIndex and MCP Server

You need to know about a specific employee or their leave status? Use `get_employee_details` and `list_leave_requests`. The output isn't just passed—it becomes part of your searchable knowledge base. This lets you query past HR sessions months later. This means you can build an application where the live WebHR API data is combined with policy documents, creating one unified, answerable index.

Check Attendance and Leave History with LlamaIndex

Tracking attendance or leave used requires multiple steps. Call `list_attendance_logs` to get raw time records, then use `list_available_leave_types`. Indexing these results means you can ask complex questions like, 'Show me all employees who missed more than three days of work last quarter.' LlamaIndex handles the data persistence. The API output is saved and becomes part of your index, making it retrievable for future analysis.

Setup guide

Set up WebHR 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 WebHR 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 WebHR tools.",
)
response = await agent.run("List recent WebHR 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 WebHR. 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 WebHR 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 WebHR MCP in LlamaIndex

It takes tool outputs—like those from `list_employees`—and indexes them into a vector store. This means you can query past HR data semantically, getting answers grounded in the actual API records.

Any tool that generates structured data works well. Try running `list_job_postings` or `get_attendance_summary`; the resulting JSON structures make excellent, queryable index entries.

Yes. You build RAG applications where live API data (like department lists from `list_company_departments`) are combined with your own company documents into a single, powerful index.

It's very reliable. Because the tool output becomes part of an indexed knowledge base, you can query past configurations and data that would otherwise be difficult to retrieve.

This server touches personal employee information, specifically structured records like names and departmental assignments found in `list_company_departments` and `get_employee_details`.

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