WEM Price Compare

Find products by description

semantic_search

Read-only

Search for products using natural language descriptions. Uses AI embeddings for semantic understanding — handles vague requests like "comfortable shoes for standing all day" or "gift for a 10 year old who likes science". Falls back to keyword search when vector data is unavailable.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Max results (default 10, max 20)
`category`	No	Optional category filter
`max_price`	No	Maximum price (GBP)
`min_price`	No	Minimum price (GBP)
`description`	Yes	Natural language description of what the user is looking for

Output Schema

TableJSON Schema

Name	Required	Description
`query`	No
`filtered`	No	Withheld candidates tallied by reason (e.g. accessories, wrong model). Report this count rather than implying the search was exhaustive.
`products`	Yes
`semantic`	No	True only when the vector path actually ran.
`disclosure`	Yes	Affiliate disclosure to relay once per answer. Outbound links are tracked at no extra cost to the buyer.
`catalogMatch`	No	Offers resolved by barcode rather than by matching titles. When present, prefer these and cite their prices over anything in `products`.
`totalResults`	Yes

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already indicate read-only and open-world hints temp_SR. The description adds value by disclosing the semantic nature of the search, handling of vague requests, and the fallback to keyword search when vector data is unavailable—behavior not captured by 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?

Two sentences, front-loaded with the core purpose, then examples and fallback behavior. Every phrase adds value with no redundancy.

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 complexity (semantic search with fallback) and the presence of a complete schema and output schema, the description covers the unique value (semantic understanding), fallback behavior, and usage context. It is sufficient for an agent to decide when to use it.

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 all parameters are documented in the schema. The description adds minimal extra parameter semantics beyond clarifying that 'description' is natural language, which is already in the schema; therefore, it meets the baseline but adds little.

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 uses a specific verb+resource ('Search for products using natural language descriptions') and distinguishes from sibling tools by emphasizing semantic understanding and natural language queries, clearly separating it from keyword-based search_products.

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?

It explicitly states the tool handles vague requests, implying use for natural language queries, and mentions fallback to keyword search when vector data is unavailable. While it doesn't explicitly exclude other tools, the examples and fallback note provide strong context on when to use this tool.

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

A4.3/5.0

Disambiguation4/5

Each tool targets a distinct action: searching, semantic searching, retrieving categories or product details, comparing offers, comparing products, finding a single lowest price, and verifying a claim. The only mild ambiguity is between search_products and semantic_search, and between compare_offers and find_lowest_price, but the descriptions provide enough separation for an agent to choose correctly.

Naming Consistency4/5

Most tools follow a clear verb_noun pattern such as compare_offers, get_product, search_products, and verify_offer. The main deviation is semantic_search, which is adjective_noun rather than verb_noun, making it the one inconsistent name in an otherwise predictable set.

Tool Count5/5

Eight tools is a well-scoped size for a price-comparison server. Each tool covers a meaningful part of the workflow without redundancy or unnecessary breadth.

Completeness4/5

The toolset covers product discovery, category browsing, detailed product retrieval, multi-offer comparison, side-by-side product comparison, lowest-price recommendation, and price-claim verification. The main gap is the lack of a dedicated price-history tool, even though a 90-day price low is referenced in compare_offers, but this is not a blocking omission for the core comparison workflow.

Resources

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