espresso-mcp
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
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| find_espresso_nearA | Find ranked specialty espresso cafes within a radius of given coordinates. Returns cafes from the curated database sorted by espresso-quality score, with distance and score reasoning for each. |
| search_cafesA | Search the curated specialty coffee cafe database by name, city, country, roaster, and minimum quality score. Returns scored results sorted by espresso-quality score descending. |
| get_cafe_detailsA | Retrieve a full curated record for a cafe by id, including the espresso-quality score breakdown (per-signal contributions) and a few nearby/related cafes. |
| score_cafeA | Apply the espresso-quality scoring algorithm to a set of observed signals (no database lookup required). Returns a 0-100 score, tier, per-signal contributions, and reasoning. Use this when you've gathered information about a cafe from a website, photo, or review and want a structured assessment. |
| list_great_roastersA | List curated specialty coffee roasters from the database, filtered by country and reputation tier. Useful for finding cafes that serve a given roaster's beans, or planning a roaster-focused trip. |
| list_anti_patternsA | List shops that exemplify what to AVOID when looking for great espresso. Includes mass-market chains (Starbucks, Dunkin', Costa) and 'flavor-led specialty' shops that display third-wave signage but lean heavily on flavored drinks. Each entry shows why it's flagged. Useful as contrast when recommending real specialty cafes, and as regression fixtures for the scoring algorithm. |
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 6 tools
Each tool has a clearly distinct purpose: get_cafe_details retrieves by ID, find_espresso_near searches by coordinates, search_cafes uses text filters, score_cafe applies the algorithm to arbitrary inputs, list_great_roasters covers roasters, and list_anti_patterns provides contrast examples. No two tools overlap in a confusing way.
All tool names follow the same verb_noun pattern in snake_case (get_, find_, search_, score_, list_). The verbs are specific and consistent with the action performed, making the naming predictable and intuitive.
Six tools is well within the ideal range for a domain-specific server. Each tool covers a distinct aspect of cafe discovery and scoring, and none feel redundant or superfluous. The count aligns with the server's focused purpose.
The toolset provides complete coverage for the espresso cafe domain: finding cafes (by location or criteria), retrieving detailed information, scoring cafes algorithmically, and accessing supporting data (roasters and anti-patterns). There are no obvious dead ends or missing core operations.