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espresso-mcp

espresso-mcp

by mattgierhart
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Score a Cafe by Observed Signals

score_cafe

Score a cafe's espresso quality from observed signals like grinder, roast, and menu. Get a 0-100 score, tier, and reasoning without a database lookup.

Instructions

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.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "observed_signals": "<observed_signals>"
    },
    "name": "score_cafe"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameNoOptional cafe name (used in reasoning and roaster lookup).
source_rankingsNoAwards / rankings the cafe appears in.
observed_signalsYesSignals observed about the cafe. Omit fields you don't know — unknowns are skipped, not penalized.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the operation (scoring algorithm), the output structure (score, tier, per-signal contributions, reasoning), and that no database lookup is required. It does not detail side effects or error behavior, but as a pure computation tool, these are not likely relevant. The description provides more than minimal transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences. The first sentence states the primary action and key constraint, the second states outputs and usage context. Every sentence is informative, no redundant or filler content. It is well structured and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (nested observed_signals object, multiple signal types, no output schema), the description covers the essential points: what it does, what it returns, and when to use it. The schema handles parameter details. The only minor omission is not explicitly stating that unknown signals are skipped, but this is covered in the schema. Overall, the description is sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters. The description only refers generically to 'observed signals' and adds no parameter-specific meaning beyond what the schema provides. It does not mislead, but it also does not enhance understanding of the parameters. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Apply the espresso-quality scoring algorithm to a set of observed signals.' It also clarifies there is no database lookup and lists concrete outputs. This clearly distinguishes it from sibling tools like get_cafe_details (which would involve lookup) and search_cafes (which finds cafes).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit context: 'Use this when you've gathered information about a cafe from a website, photo, or review and want a structured assessment.' It also adds 'no database lookup required,' implying a contrast with database-backed tools, though it does not explicitly name alternatives or exclusion criteria. This is clear but lacks explicit 'when not to use' guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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