Agent-native web protocol — expose your backend directly to AI agents
AgentGate provides 8 tools with basic schemas and descriptions, but significant gaps limit production readiness. Tool naming follows verb_noun convention (search_restaurants, order_food, track_delivery), which is good. However, descriptions are minimal (10-50 chars), parameter descriptions are absent, and output schemas are not documented. The framework supports flows with multi-step orchestration, but tool definitions lack the depth needed for reliable LLM reasoning. No error handling guidance, no parameter constraints (enums), and no indication of idempotency or side effects.
End-to-end food ordering
Place the order
Search for restaurants
View menu for a selected restaurant
Search restaurants by query and city
Track a placed order
Get current delivery status
Parent flow tools (order_food, track_delivery) lack input schemas and descriptions. LLMs cannot determine when to invoke them or what parameters they accept.
No parameter descriptions provided. LLMs cannot infer what 'query', 'location', 'restaurant_id', 'order_id', 'tracking_id' mean or what format they expect.
Output schemas not documented. Responses return Pydantic models (Restaurant, MenuItem, OrderConfirmation, etc.) but LLMs have no visibility into field names, types, or structure.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 40 | 2026-07-28+ | v2 |
Start tracking — returns a tracking_id
No error handling guidance. Tools do not indicate what errors are retryable, what user input is needed, or how to recover from failures.
No indication of side effects or idempotency. 'order_food/checkout' and 'track_delivery/initiate' are write operations but lack descriptions stating they modify state or are non-idempotent.