mcp-retail-sim
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- AlicenseAqualityCmaintenanceProvides an in-memory mock commerce backend for shopping agents, enabling product search, order creation, simulated payment, automatic shipping/delivery progression, and refund workflows. It lets developers test full shopping-agent lifecycles deterministically without real products, credentials, or funds.7MIT
- AlicenseNot gradedqualityBmaintenanceEnables read- or write-scoped access to synthetic enterprise orders, inventory, support tickets, and metrics via schema-validated JSON-RPC tools over stdio and HTTP, with permission allow-lists and audit logging. It also exposes enterprise resources and prompt templates for grounded, cited, multi-step assistant workflows.MIT
- AlicenseNot gradedqualityBmaintenanceExposes a fictional smart-home store's REST API to agents as schema-validated MCP tools, resources, and prompts, letting them search products, check inventory, place atomic orders, look up customers and orders, and open support tickets. It also adds auth injection, rate limiting, REST-to-MCP error mapping with actionable hints, per-call JSONL tracing, and a golden-task eval harness for tool selection and argument correctness.MIT
- FlicenseNot gradedqualityDmaintenanceEnables AI agents to perform e-commerce operations including product search, budget-constrained shopping recommendations, and sustainability analysis. Includes a secure HTTP bridge with OAuth integration and observability features for production deployment.-
- FlicenseNot gradedqualityCmaintenanceEnables AI agents to provide e-commerce customer service with product search, RAG-based FAQ answers, human handoff, and automated security auditing with a review workflow.-
- AlicenseNot gradedqualityCmaintenanceEnables 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.MIT
TDQS
Scored across 6 tools
Each tool targets a distinct action: catalog search, single-product lookup by SKU, stock check, cart addition, checkout, and order read. The boundaries between get_product_details and check_inventory are clarified by descriptions (one returns product data, the other returns stock_qty/in_stock).
Consistent snake_case with a verb_noun pattern for five tools (get_product_details, check_inventory, add_to_cart, place_order, get_order_details). The lone exception is 'search', which lacks an explicit noun/target, a minor deviation from the otherwise predictable pattern.
Six tools is well-scoped for a retail simulation, covering the essential browse-to-checkout flow without redundancy. Each tool earns its place and none feels like filler.
The core lifecycle (search, inspect, inventory, cart, order) is covered end-to-end. Minor gaps exist: no cart inspection/removal, no order listing or cancellation, which an agent could work around for a simple sim.