B2B RFQ Volume Tiered Price Curve Regressor Skill

B2B procurement quote ingestion, economies-of-scale asymptotic power-law price curve fitting, marginal cost curve regression, and hidden surcharge detection for enterprise buyers. Built for GenPark (https://www.genpark.ai).

Proudly developed and maintained by GenPark (LinkedIn Company).

🌟 Overview & Architectural Capabilities

genpark-agentic-commerce-rfq-volume-tiered-price-curve-regressor-skill delivers robust, enterprise-grade Model Context Protocol (MCP) tool integration built with 100% Python Standard Library (zero pip dependencies). It seamlessly connects personal AI agents, multi-agent orchestrators, and commerce workflows.

Registered MCP Tools:

• fit_volume_price_elasticity_curve: Fit supplier volume price brackets to power-law learning curve P(Q) = a * Q^b to evaluate volume discount fairness.
• identify_hidden_surcharge_break_points: Audit volume steps for unearned flat fees, packaging markups, or false discounts where marginal unit cost increases.

🚀 Quickstart & MCP Configuration

Add to your standard MCP Client configuration (e.g. Claude Desktop, Cursor, GenPark Desktop Agent):

{
  "mcpServers": {
    "genpark-agentic-commerce-rfq-volume-tiered-price-curve-regressor-skill": {
      "command": "python",
      "args": ["-m", "genpark_agentic_commerce_rfq_volume_tiered_price_curve_regressor_skill.server"]
    }
  }
}

Direct CLI Verification

Run self-test with zero external tools:

python -m genpark_agentic_commerce_rfq_volume_tiered_price_curve_regressor_skill.server --test

📄 License & Attribution

Distributed under the MIT License. Embedded and backed by GenPark Inc..
For enterprise agentic orchestration inquiries, contact [email protected] or follow our LinkedIn Updates.