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talvola

bar-assistant-mcp

by talvola

bar_uses_for_bottle

Find recipes that accept a given bottle by checking declared slot constraints. Use it before adding a new ingredient to your shelf.

Instructions

Given a bottle, list recipes (with declared slot constraints) that welcome it.

Useful when a new bottle arrives — find which existing constrained recipes welcome it before adding the bottle to your shelf.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_nNo
ingredient_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose meaningful semantics: results are limited to 'constrained' recipes and describe slot-based inclusion. It omits whether the operation is read-only, how results are ordered/paginated, or the effect of top_n, leaving notable behavioral gaps.

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?

Two tight, front-loaded sentences that lead with the core action before the use-case. The clause 'welcome it' is repeated in the second sentence, a minor redundancy, but overall there is little wasted text.

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?

An output schema exists, so return values need no explanation, and the description adequately conveys the tool's intent and the 'declared slot constraints' scoping. The main gap is the undocumented top_n parameter, which an agent cannot interpret from text alone.

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

Parameters2/5

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

'Given a bottle' implicitly maps to ingredient_id, adding a little meaning. But schema coverage is 0% and the description never explains top_n or the default of 10, so with two undocumented parameters it only partially compensates for the schema gap.

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

Purpose4/5

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

It states a specific verb (list) and resource (recipes that welcome a given bottle), scoped by 'declared slot constraints', which is more precise than a generic ingredient lookup. It does not explicitly distinguish itself from close siblings like bar_ingredient_cocktails or bar_alternatives_for_slot, so the distinction is left to inference.

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 second sentence gives a clear when-to-use scenario ('when a new bottle arrives... before adding the bottle to your shelf'). However, it names no alternatives or exclusions, so an agent must still guess why to pick this over bar_ingredient_cocktails.

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