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Cellarion Wine Registry

by app.cellarion

Find similar wines ("more like this")

find_similar_wines
Read-only

Given a registry wine_id (or, on an authenticated connection, one of the user's bottle_ids), returns wines with the closest taste/style profile from the shared registry, using vector similarity over wine embeddings. Call for "more like this", "what else is like my favourite Barolo", or to seed purchase ideas from a wine the user loves. Only wines that have been embedded are searchable — an empty result does not mean nothing similar exists. Ids must be 24-hex Mongo ids from search_registry or search_bottles — a name or slug is not an id. Returns at most 10.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many to return (1-10, default 8; larger values are capped, not rejected)
wine_idNoRegistry wine id (24-hex) from search_registry or a bottle's wine
bottle_idNoAlternatively: one of the user's bottle ids (24-hex). Needs an authenticated connection — not available on the public endpoint.

TDQS

A4.9/5.0
Behavior5/5

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

The description adds context beyond the readOnlyHint annotation, such as the vector-similarity mechanism, authentication requirement for bottle_id, the caveat that an empty result does not prove absence of similar wines, and the id format restriction. This gives the agent a clear behavioral model without any contradiction with the annotations.

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 three sentences that pack essential details: function and inputs, use cases, and critical caveats. It is front-loaded with the primary purpose and avoids repetition or filler, making it efficient for an agent to parse.

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

Completeness5/5

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

Given the tool's modest complexity (3 optional params, read-only, no output schema), the description covers all necessary operational aspects: input origins, authentication, result count cap, and interpretation of empty results. It is sufficiently complete for an agent to invoke the tool correctly without ambiguity.

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?

While the schema already covers all parameters (100% coverage), the description adds important semantics: wine_id vs. bottle_id roles, the need for an authenticated connection for bottle_id, and that ids must be Mongo hex ids from specific sources. This goes beyond the schema's basic descriptions, though limit is only lightly touched ('at most 10').

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 function: 'returns wines with the closest taste/style profile from the shared registry, using vector similarity over wine embeddings'. It specifies the input types (wine_id or bottle_id) and output (up to 10 similar wines), distinguishing it from sibling tools by focusing on similarity search rather than generic registry lookup.

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

Usage Guidelines5/5

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

The description explicitly says when to use the tool: 'Call for "more like this", "what else is like my favourite Barolo", or to seed purchase ideas'. It also provides a clear exclusion: ids must be 24-hex Mongo ids from search_registry/search_bottles, not names/slugs, and notes the limitation about embedded wines, giving practical guidance on interpreting empty results.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool addresses a distinct concern: registry search, wine detail, vintage-specific drink windows, similarity recommendations, guide listing, guide reading, and server metadata. No two tools serve the same purpose, and the descriptions clearly indicate when to use each.

Naming Consistency4/5

Most tools follow a consistent verb_noun pattern (list_guides, read_guide, search_registry, get_wine, get_source_info, find_similar_wines). 'drink_window_for' is an outlier, using a noun phrase instead of an imperative verb, which introduces a minor inconsistency.

Tool Count5/5

With seven tools, the server is well-scoped and avoids bloat. Each tool provides a distinct capability, and the count is appropriate for a wine registry server covering search, details, drink windows, similar wines, guides, and metadata.

Completeness4/5

The core registry workflow is well covered: search_registry for identification, get_wine for details, drink_window_for for aging advice, and find_similar_wines for recommendations. However, multiple descriptions reference search_bottles for user-owned bottles, yet that tool is not provided, leaving a notable gap for user-specific queries.

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