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search_bottles

Read-onlyIdempotent

Full-text search across 4,700+ bourbons, ryes, scotches, and other spirits in the Pour Picks database. Filter by category, price (USD), and proof. Returns structured tasting profiles with source attribution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 10, max 10)
queryNoFree-text search: distillery, bottle name, or expression
categoryNoSpirit category filter
price_maxNoMaximum price in USD
price_minNoMinimum price in USD
proof_maxNoMaximum proof
proof_minNoMinimum proof

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / limit / description
      Previous value: -"Max results (default 10)"New value: +"Max results (default 10, max 10)"
    • changedInput schema / properties / limit / maximum
      Previous value: -25New value: +10
  2. Changed1 schema field changed
    • addedInput schema / properties / query / maxLength
      Added value: +120
  3. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint=false, and destructiveHint=false, so safety behavior is covered structurally. The description adds useful context (corpus size, return shape 'structured tasting profiles with source attribution') but says nothing about result ranking, empty-result behavior, or why the cap is 10. Solid but not rich.

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?

Two tight sentences with zero filler. The core capability and scope lead, filters and return shape follow, and no sentence duplicates structured fields verbatim.

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?

With no output schema, the description usefully discloses the return shape ('structured tasting profiles with source attribution'), which is what an agent most needs here. It is nearly complete for a read-only search tool; only ranking/ordering and behavior on zero matches are left unstated.

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 every parameter (query, category enum, price_min/max, proof_min/max, limit) is already documented with types, bounds, and defaults. The description only restates that category, price, and proof are filterable, adding no format or interaction semantics (e.g. how query interacts with category). Baseline 3 applies.

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?

States a specific verb (full-text search) and resource (spirits catalog), plus scope details ('4,700+ bourbons, ryes, scotches'). An agent can distinguish this from get_bottle (single lookup) or compare_bottles by the word 'search', but the description never names a sibling to sharpen the boundary.

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?

The phrase 'Full-text search' implies the usage context of exploratory/query-based retrieval, and the filter list hints at narrowing use cases. However, there is no explicit when-to-use versus find_similar, get_recommendations, or find_cheaper_alternative, and no stated prerequisites or exclusions.

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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