Blue Perfumery MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_all_perfumesB | List all perfumes in the Blue Perfumery collection |
| get_perfume_by_idB | Get a specific perfume by its ID |
| search_perfumesC | Search perfumes by name or brand |
| get_perfumes_by_categoryB | Get perfumes by category (men, women, niche) |
| get_purchase_linkB | Get the Shopier purchase link for a specific perfume |
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
Most tools have distinct purposes: get_by_id, get_by_category, list_all, and search target different retrieval methods, while get_purchase_link is unique. However, list_all_perfumes and search_perfumes could overlap in some cases if an agent wants to browse versus search, but descriptions clarify their intents.
All tool names follow a consistent verb_noun pattern with snake_case, using verbs like 'get', 'list', and 'search' appropriately. The naming is predictable and readable across all five tools.
With 5 tools, this server is well-scoped for a perfumery collection, covering essential retrieval operations without being overly complex. Each tool serves a clear purpose, making the count appropriate for the domain.
The toolset is read-only, focusing on retrieval and search, which is reasonable for a catalog server. However, it lacks any write operations (e.g., create, update, delete) or advanced features like filtering by price or rating, which could limit agent functionality in broader e-commerce contexts.