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mcp/skillhub

Hiring Fairness & Selection ROI MCP Server

ai.saifs/hiring-fairnessv1.0.0

Detect hiring adverse impact and estimate whether a selection method is worth its cost.

remoteofficial registry

context tax

pending

security

pending

cold start

hosted

freshness

pending

Our reading of Hiring Fairness & Selection ROI, from the data

Hiring Fairness & Selection ROI is an MCP server, listed in the official MCP Registry.

We haven't measured its tool list yet, so its token cost is unknown.

It runs as a hosted endpoint, so there is nothing to install.

Install Hiring Fairness & Selection ROI MCP server

Install in Claude Code

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

Remote endpoints

  • streamable-httphttps://hiring-fairness-mcp.saifs.ai/mcp

Freshness

Not enough public signals yet to judge maintenance.

  1. Last commit (default branch)

    no data · GitHub

  2. Latest release

    no data · GitHub

  3. Package published

    no data · npm/PyPI

  4. Registry entry updated

    2026-10-16 · 2d ago · official registry · v1.0.0

FAQ

›How do I install the Hiring Fairness & Selection ROI MCP server in Claude Code?

Run: claude mcp add --transport http hiring-fairness https://hiring-fairness-mcp.saifs.ai/mcp. For Cursor, VS Code, Claude Desktop and Windsurf, use the install tabs above.

›Does Hiring Fairness & Selection ROI require an API key?

No required environment variables are declared in its published metadata.

›Can I use Hiring Fairness & Selection ROI as a remote (hosted) MCP server?

Yes. It is a hosted MCP server; connect to its URL with any client that supports remote MCP.

›Is Hiring Fairness & Selection ROI in the official MCP registry?

Yes, as ai.saifs/hiring-fairness.

Sources and method

Official MCP Registry entry
updated 2026-10-16
Last checked against all sources
2026-10-19

The description, packages and links come from the maintainer's own published metadata. Token cost, security signals, freshness and the reading above are MCPSkillHub's own measurements; see the methodology. Spot an error? Request a correction or claim the listing.