cafe-mcp-server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PORT | No | When set, the server uses Streamable HTTP and binds to 0.0.0.0:$PORT, exposing MCP at /mcp. | |
| MCP_TRANSPORT | No | Transport to force: 'stdio' or 'streamable-http'. If not set, defaults based on PORT. |
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 | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_coffee_menuA | List all available coffee menu items. |
| recommend_coffeeA | Recommend a coffee from mood and simple preferences. Args: mood: Free-text preference such as "smooth iced" or "quick energy". prefer_milk: True for milk drinks, False for black coffee, None for either. caffeine: Optional caffeine level: low, medium, or high. temperature: Optional drink style: hot or cold. |
| explain_recommendationC | Explain a coffee choice against the same preference inputs. |
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 3 tools
The tools are mostly distinct: listing the menu, recommending a coffee, and explaining a recommendation. There is slight potential for confusion between recommend_coffee and explain_recommendation, but their purposes are clear enough to avoid misselection.
All tool names follow a consistent verb_noun snake_case pattern: list_coffee_menu, recommend_coffee, explain_recommendation. This makes the API predictable and easy to navigate.
With 3 tools, the server is on the smaller side but well-scoped for its purpose. Each tool serves a distinct function in the coffee recommendation workflow, and the count does not feel excessive or incomplete.
The server covers the core domain of browsing, recommending, and explaining coffee choices. It lacks advanced features like detailed item descriptions or user feedback loops, but these are not essential for a basic cafe menu assistant.