Uber Eats MCP Server
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- FlicenseNot gradedqualityDmaintenanceEnables interaction with Uber Eats for food ordering and delivery management through natural language, acting as a proof-of-concept MCP integration.-
- AlicenseAqualityCmaintenanceEnables an AI assistant to browse restaurants and menus, manage a cart, reorder past orders, review checkout details, and place food delivery orders, returning a payment link to complete payment.13MIT

Canuckeats MCPofficial
FlicenseNot gradedqualityDmaintenanceEnables AI agents to browse menus and place real food delivery orders from UberEats and DoorDash across 89 Canadian cities.1-- AlicenseNot gradedqualityDmaintenanceEnables AI agents to autonomously order food from Grubhub, including searching restaurants, browsing menus, managing cart, placing orders, and tracking delivery.17 npm1MIT
- FlicenseNot gradedqualityDmaintenanceA proof-of-concept Model Context Protocol server that enables LLM applications to interact with Uber Eats, allowing AI agents to browse and order food through natural language.240-
- AlicenseAqualityDmaintenanceEnables an AI agent to search stores and dishes, browse menus, add items with options and quantities, and read a checkout snapshot inside the user's own logged-in Chrome session, placing an order only after explicit approval. Ordering is gated behind a review step with a one-time confirm token, spend caps, and post-order delivery tracking, while card numbers and addresses never appear in the conversation.812MIT
TDQS
Scored across 34 tools
Most tools are clearly separated by domain (address, cart, checkout, orders, search, preferences), but there are some ambiguous boundaries. uber_eats_get_address vs uber_eats_saved_addresses vs uber_eats_set_address overlap conceptually, and uber_eats_track_orders vs uber_eats_track_active_orders overlap heavily. Checkout tools (checkout_preview, checkout_savings, set_checkout_tip, set_checkout_payment) are distinct but close enough that an agent could misselect.
All tools follow the uber_eats_ prefix with consistent snake_case. Most use verb_noun structure (search, track, view, remove, set). Minor inconsistencies: 'whoami' and 'recommend' and 'reorder' lack a noun object, and 'build_taste_profile' uses adjective rather than a direct action on a noun. Overall naming is quite predictable.
34 tools is on the heavier side. The domain genuinely has many facets (search, browsing, cart, checkout, payment, orders, preferences, recommendations, profiles, favorites), so the count is defensible, but it borders on excessive and could clutter an agent's tool selection. Some tools like track_orders vs track_active_orders could be merged.
The coverage is thorough: search, browse, menu, items, cart CRUD, checkout flow, payment, promo, tips, orders tracking, reordering, profiles, preferences, favorites, and recommendations. Minor gaps include lack of explicit remove-payment-method or a dedicated order-cancel tool, and reorder doesn't fully perform the action (only surface items for re-add). Overall the lifecycle from search through ordering to tracking is well covered.