Hiring Fairness & Selection ROI

Hiring Fairness & Selection ROI

Group-level fairness and value analysis of a hiring process. From counts per stage and group it returns selection rates and impact ratios at every stage (EEOC four-fifths rule), whether each gap is larger than chance, the end-to-end funnel, and a like-for-like check across jobs that catches Simpson's paradox. From anonymous score/outcome pairs it tells you whether a test predicts job success, whether it underrates a group, a fair pass mark, and its money value net of cost. It never scores, ranks or selects individuals, and its output is analysis, not legal advice.

Registry name	ai.saifs/hiring-fairness
Endpoint	https://hiring-fairness-mcp.saifs.ai/mcp
Transport	Streamable HTTP (remote, nothing to install)
Auth	OAuth 2.1 — PKCE (S256) and dynamic client registration; your client opens a sign-in page on first use
Publisher	SAIFS AI

Install

VS Code / GitHub Copilot

Add to .vscode/mcp.json (workspace) or your user mcp.json:

{
  "servers": {
    "saifs-hiring-fairness": {
      "type": "http",
      "url": "https://hiring-fairness-mcp.saifs.ai/mcp"
    }
  }
}

Then start the server from the MCP view and sign in when prompted.

Claude (claude.ai, Desktop)

Settings → Connectors → Add custom connector, and paste:

https://hiring-fairness-mcp.saifs.ai/mcp

Claude Code

claude mcp add --transport http saifs-hiring-fairness https://hiring-fairness-mcp.saifs.ai/mcp

Any other MCP client

Point it at https://hiring-fairness-mcp.saifs.ai/mcp using the Streamable HTTP transport. The server returns 401 with a WWW-Authenticate: Bearer … resource_metadata=… challenge, so OAuth-aware clients sign in automatically.

Tools

Tool	What it does
hiring_fairness	Analyses up to 5,000 rows of stage counts (or stage/group/selected candidate rows, or anonymous score/outcome pairs) and returns decision cards such as REVIEW THIS STAGE, COMPARE LIKE WITH LIKE, TEST PREDICTS, SET CUTOFF and WORTH THE COST.
hiring_fairness_get_engine	For larger ATS exports: returns a numpy-only Python script that aggregates candidate rows to group counts locally and runs the same analysis, so no candidate data reaches the server.

Example prompts

• “Here are our applicant and hire counts by stage and gender. Is any stage showing adverse impact?”
• “Is our coding assessment worth what it costs, and what pass mark is fair?”
• “Compare selection rates across departments like for like.”

Support

• Website: https://saifs.ai
• Issues: https://github.com/Saify-Technologies/saifs-ai-mcps-docs/issues
• Privacy: https://saifs.ai/privacy_policy