HRIS MCP Connector
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@HRIS MCP Connectorwho joined in the last two weeks?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
HRIS MCP Connector
A Model Context Protocol (MCP) server that gives an AI client like Claude Desktop read-only access to HR information system data through a typed tool layer. Built with the official Python MCP SDK.
This public repository is a reference implementation backed by a synthetic mock data layer. The production version swaps that layer for authenticated calls to a real HRIS such as Rippling, leaving the tool surface identical. No real employee data, no credentials, and no vendor-specific configuration are included here.
What It Does
It exposes four read-only tools to an MCP client:
Tool | What it returns |
| Employees who started within the last N days, newest first |
| A single employee record by ID |
| Each department with its current headcount |
| Remaining paid-time-off hours for one employee |
Ask Claude something like "who joined in the last two weeks?" and it calls
list_recent_hires and answers from the result.
Related MCP server: hr-faq-rag
Why It's Built This Way
Read-only by design. Every tool retrieves information; none changes HR data. Write actions belong behind authentication, authorization, audit logging, and human approval, which are deliberately out of scope for a public reference server.
A clean tool surface over a swappable data layer. The MCP tools call plain functions
in mock_data.py. In production, that one module is replaced with real HRIS API calls and
nothing else changes. That separation is the whole point: the AI client sees a stable
contract regardless of what's behind it.
Testable without a server. Because the query logic lives in plain functions, the test
suite checks it directly with a fixed reference date. No process, no network, no flakiness.
See tests/.
Protocol-safe logging. This is a stdio server, so stdout carries the protocol. All diagnostics go to stderr, which keeps the message stream clean.
Architecture
flowchart LR
C[Claude / MCP Client] --> S[MCP Server: tools]
S --> D[Data Layer]
D --> M[Mock Data]
D -. production .-> API[Real HRIS API]Setup
pip install -r requirements.txtRun the server:
python src/server.py
Connect it to Claude Desktop
Add this to your Claude Desktop config file, using the absolute path to this repo:
{
"mcpServers": {
"hris-connector": {
"command": "python",
"args": ["/absolute/path/to/hris-mcp-connector/src/server.py"]
}
}
}Restart Claude Desktop fully (quit, don't just close the window). The four tools then appear in the client.
Going to Production
To connect a real HRIS, replace the functions in src/mock_data.py with authenticated API
calls. The API token goes in a .env file (see .env.example), which is gitignored and
never committed. The tool definitions in src/server.py stay exactly as they are.
Status
Reference implementation with a mock data layer. A production version of this connector runs against a live HRIS in a real HR environment; this public repository contains the pattern and the mock layer only.
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Latest Blog Posts
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/builtbybianca/hris-mcp-connector'
If you have feedback or need assistance with the MCP directory API, please join our Discord server