Hopi

Event wine calculator

hopi_event_wine_calculator

Read-onlyIdempotent

Work out how many bottles of red, white and Champagne to buy for an event, from the number of guests and glasses per guest. Ticking different wines splits a colour into one wine per glass, each poured once for every guest. Source: https://hopi.co.uk/event-wine-calculator/

Input Schema

TableJSON Schema

Name	Required	Description
`guests`	Yes	Number of guests
`redSize`	No	Red glass size in ml: 125, 175 or 250
`whiteSize`	No	White glass size in ml: 125, 175 or 250
`redGlasses`	No	Glasses of red per guest
`redDifferent`	No	Serve a different red for each glass
`whiteGlasses`	No	Glasses of white per guest
`sparklingSize`	No	Sparkling glass size in ml: 100, 125 or 150
`whiteDifferent`	No	Serve a different white for each glass
`sparklingGlasses`	No	Glasses of Champagne or sparkling per guest
`sparklingDifferent`	No	Serve a different sparkling for each glass

Output Schema

TableJSON Schema

Name	Required	Description
`guests`	No
`summary`	Yes
`breakdown`	No
`overageMl`	No
`source_url`	Yes	The tool's page on hopi.co.uk (cite or open to check the figure)
`totalBottles`	No
`glassesPerGuest`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 20, 2026
- addedOutput schema / required
```
Added value: +[
+  "source_url",
+  "summary"
+]
```
First observedSep 20, 2026

TDQS

A3.9/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is a safe, non-mutating calculation. The description adds the behavioral detail that 'ticking different wines splits a colour into one wine per glass, each poured once for every guest,' which clarifies the calculation logic beyond the schema. However, it doesn't disclose rounding behavior, bottle size assumptions, or whether results are per bottle size (e.g., 75cl standard), which would be useful for a calculator tool.

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

Conciseness4/5

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

The description is two sentences and front-loads the core purpose. The first sentence is a clear, actionable statement of what the tool does. The second sentence adds a useful behavioral nuance about the 'different wines' option. The source URL is a minor addition that could be considered extraneous, but it doesn't detract significantly. It's efficient and well-structured.

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 has an output schema (not shown but indicated), the description doesn't need to explain return values. The annotations cover safety (read-only, idempotent, non-destructive). The description covers the core inputs (guests, glasses per guest) and the key behavioral nuance (splitting colours). The only gap is the lack of explicit mention of bottle size assumptions or rounding, but for a simple calculator with a rich schema, this is adequate.

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 10 parameters with clear descriptions (e.g., 'Red glass size in ml: 125, 175 or 250'). The description adds the conceptual meaning of the 'Different' boolean parameters ('splits a colour into one wine per glass'), which is valuable. However, it doesn't explain the relationship between glass size and bottle count, or any defaults for optional parameters, so it doesn't fully compensate beyond the schema's already strong coverage.

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 clearly states the tool's purpose: 'Work out how many bottles of red, white and Champagne to buy for an event, from the number of guests and glasses per guest.' It uses a specific verb ('work out'), names the resource (bottles of wine for an event), and distinguishes it from the many sibling calculators by focusing on event wine quantities. The additional sentence about splitting colours into one wine per glass adds useful specificity.

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 implies the primary use case: planning wine purchases for an event based on guest count and glasses per guest. It doesn't explicitly state when not to use it or name alternatives, but the context of the sibling tools (all different calculators) makes the usage context reasonably clear. The mention of 'ticking different wines' gives a hint about optional parameters, but there's no explicit exclusion or alternative routing.

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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