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Pour Picks

by io.github.bguillow-rgb
OverviewDiscussionsAdmin

get_recommendations

Read-onlyIdempotent

Personalized bottle picks from taste preferences (flavor keywords like 'caramel', 'smoke', 'cherry'), a budget in USD, and an occasion (e.g. 'gift', 'everyday sipper', 'celebration', 'introducing a friend to bourbon').

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": {
      "taste_preferences": "<taste_preferences>"
    },
    "name": "get_recommendations"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax results (default 5)
budgetNoMax price in USD
categoryNoRestrict to one spirit category
occasionNoWhat the bottle is for
taste_preferencesYesFlavor keywords the drinker enjoys, e.g. ['caramel','vanilla','oak']

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / occasion / maxLength
      Added value: +120
    • addedInput schema / properties / taste_preferences / items / maxLength
      Added value: +60
    • addedInput schema / properties / taste_preferences / maxItems
      Added value: +20
  2. First observed

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so the safety profile is covered. The description adds no behavioral detail beyond that - nothing about how picks are ranked, how many come back by default, or result variability - leaving only modest added value.

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?

A single front-loaded sentence that leads with the outcome ('Personalized bottle picks') before listing drivers. The parenthetical example lists are slightly long but each illustration adds selection signal, so little is wasted.

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

Completeness3/5

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

With five parameters and no output schema, the description should carry more burden in describing what a 'recommendation' returns (fields, count, ranking basis), which it omits. It covers inputs adequately but leaves the result shape undefined for an agent.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description enriches semantics by supplying concrete flavor keywords ('caramel', 'smoke', 'cherry') and richer occasion examples beyond the schema's generic 'What the bottle is for'. Budget is explicitly tied to USD, clarifying units.

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?

The description names a specific output ('personalized bottle picks') and the inputs that drive it (taste preferences, budget, occasion), so the verb+resource is unambiguous. It stops short of distinguishing itself from siblings like search_bottles or trending_bottles, which an agent would need to disambiguate a recommendation request.

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

Usage Guidelines3/5

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

Use is implied through the enumerated inputs and occasion examples ('gift', 'celebration', 'introducing a friend to bourbon'), which convey the personalized-pick scenario. There is no explicit when-to-use versus alternatives or any exclusions, so guidance remains inferential.

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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