"Agentic RAG: Understanding or Exploring Its Meaning and Applications" matching MCP servers:

An MCP server for LLM-driven commerce that lets agents shop in real storefronts while enforcing a trust layer to prevent financial loss.

A custom MCP server that turns Claude into an agentic shopping assistant for searching products, comparing options, managing a cart, and checking out with built-in guardrails for safe autonomous commerce.

Enables AI agents to autonomously browse inventory, negotiate terms, manage carts, and execute secure payments on Shopify stores using standardized protocols. It provides a bridge for LLMs to handle the entire commerce lifecycle from discovery to order tracking through a verifiable mandate chain.

MCP server that mimics a food delivery assistant, enabling natural language ordering, personalized recommendations, order tracking, and review/policy queries via a LangGraph ReAct agent with human-in-the-loop approval.

MCP server that exposes customer profile, order listing, and order cancellation tools, integrating with underlying REST services to enable AI agents to manage customer and order workflows.

Enables AI agents to browse a merchant catalog and place Razorpay test-mode orders with budget gating, persistent monthly limits, and full audit trails.

Enables e-commerce customer service agents to retrieve product catalog and return/delivery policy documents through multi-source RAG with RRF fusion, exposing product_search and policy_qa tools for grounded, cited responses.

Klarna-style product discovery for AI shopping agents. Makes product catalogs machine-readable so AI agents can search, compare, and purchase products programmatically.

Enables users to ingest carrier webhook events, detect transit anomalies such as stalled or failed deliveries, calculate compensation eligibility, and generate structured merchant claim payloads.

Enables agents to ingest carrier webhook events, track stalled or failed shipments, evaluate compensation eligibility, and emit structured merchant claim payloads over MCP. It runs as a zero-dependency Python JSON-RPC stdio server or importable module for post-purchase dispute arbitration.

Enables agents to translate natural-language shopping intent into aggregated multi-merchant item discovery, consolidated cross-merchant carts, and structured transaction checkout payloads. Runs as a zero-dependency, MCP-compatible engine that can also be imported directly as a Python module.

Enables interactive restaurant discovery and ordering through a synthetic commerce flow with rich HTML UI. Demonstrates agentic commerce UX with tools to find restaurants, view menus, place mock orders, and generate receipts.

Enables agents to translate consumer shopping intent into aggregated item discovery, cross-merchant cart consolidation, and structured transaction checkout payloads across multiple merchants. It runs as a zero-dependency Python MCP server or importable module compatible with Claude Desktop, Cursor, and other work agent frameworks.

Enables agents to forecast SKU sales velocity, calculate safety stock and reorder points, and autonomously generate supplier purchase orders when stock is running low. It exposes this supply-chain analysis over JSON-RPC 2.0 tools that can be called from any MCP-compatible client.

Enables users to register post-purchase receipts, monitor retailer price drops within price-match guarantee windows, verify eligibility, and synthesize automated refund claims.

Complete Vinted MCP and CLI Server

The official Model Context Protocol adapter for Salesforce Commerce Cloud (B2C). Empower your AI agents to seamlessly navigate headless storefronts, manage e-commerce orders, and interact with complex SCAPI/OCAPI endpoints natively.

This MCP wraps Bitrefill public API to allow agents to search for products and shop using cryptocurrencies like Bitcoin, Ethereum, Solana, and many more.

Agentic MCP for buying VISA/MasterCard cards, VISA prepaid cards, eSIM and Gift Cards (to pay for 20+ services). Top up with USDT TRC-20.

Enables forecasting SKU demand with exponential moving averages, computing safety-stock buffers and reorder points, and generating supplier purchase orders through an MCP JSON-RPC 2.0 interface.