Swiggy AI Insights MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| NODE_ENV | No | Set to 'production' for production mode, otherwise uses development defaults. | development |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| sync_ordersB | Sync orders from Swiggy API to persistent storage |
| get_ordersC | Get orders from persistent storage with optional filtering |
| get_restaurantsB | Get all restaurants with order counts and spending stats |
| get_analyticsC | Get comprehensive analytics from persistent storage |
| search_ordersA | Search orders by restaurant name, cuisine, or location |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Each tool has a distinct role: ingest, query, restaurant stats, analytics, and text search. The only potential confusion is between get_orders and search_orders, but the descriptions clarify that one is for structured filtering and the other for text-based search.
All tools follow a consistent verb_noun pattern using snake_case (sync_orders, get_orders, get_restaurants, get_analytics, search_orders). The verbs are clear and the pattern is predictable.
With 5 tools, the set is well-scoped for the server's purpose of syncing Swiggy data and providing insights. Each tool earns its place without redundancy or unnecessary bloat.
The core workflow of syncing orders, querying orders, viewing restaurant stats, and getting analytics is covered. Minor gaps exist such as no explicit restaurant sync or order update/delete, but these are likely intentional for an analytics-focused server.