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mattgierhart

espresso-mcp

by mattgierhart

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

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

CapabilityDetails
tools
{
  "listChanged": true
}

Tools

Functions exposed to the LLM to take actions

NameDescription
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

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

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