Dynamic Pricing & Overbooking

Dynamic Pricing & Overbooking

Yield-management math over booking, capacity and price data for perishable capacity — hotel rooms, event tickets, rental equipment, clinic slots, restaurant covers. Recommends the price now, protection levels for higher-value segments, elasticity, a safe overbooking limit, and alerts when booking pace runs ahead or behind. Returns ranges and the probability of denying service; it does not auto-execute prices.

Registry name	ai.saifs/revenue-optimization
Endpoint	https://revenue-optimizer-mcp-microsoft.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-revenue-optimization": {
      "type": "http",
      "url": "https://revenue-optimizer-mcp-microsoft.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://revenue-optimizer-mcp-microsoft.saifs.ai/mcp

Claude Code

claude mcp add --transport http saifs-revenue-optimization https://revenue-optimizer-mcp-microsoft.saifs.ai/mcp

Any other MCP client

Point it at https://revenue-optimizer-mcp-microsoft.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
denied_service_risk	Probability and expected number of customers turned away at a booking limit.
estimate_elasticity	How demand responds to price from price and sales history.
explain_recommendation	The revenue-management logic and assumptions behind a recommendation.
optimal_overbooking	Booking limit that maximises expected profit given show-up rate.
optimize_price	Recommended price now from remaining capacity, days to deadline and demand.
protection_levels	How much capacity to protect for higher-value segments (Littlewood / EMSR).
recommend_overbooking	Safe overbooking level from no-show rate, capacity and costs.
show_rate_estimate	Show-up rate by route, day or segment with confidence.
watch_booking_pace	Alert when booking pace runs ahead or behind, triggering price moves.

Example prompts

• “How many rooms should we overbook?”
• “Set fare buckets for next week”
• “What price maximizes revenue?”

Support

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