Find products similar to a given product using vector similarity. Returns up to 10 nearest neighbours by semantic meaning (title+description embedding). Useful for "more like this" recommendations.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of similar products to return (1-10, default 10) | |
| product_id | Yes | UUID of the source product |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral transparency burden. It discloses that results are based on semantic meaning using title+description embeddings and that it returns up to 10 nearest neighbours. This is meaningful behavioral context, though it does not cover edge cases such as unknown product IDs or empty result sets.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded: the core action and method appear in the first sentence. Every sentence adds useful information: return count, similarity basis, and the intended recommendation use case.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool with no output schema and no annotations, the description covers purpose, algorithm, result cap, and application context. It is not fully exhaustive about result fields or error behavior, but it is sufficient for an agent to select and invoke the tool correctly in most cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds algorithmic context but does not clarify parameter semantics beyond what the schema already provides: product_id is the source product and limit is the number of results.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Find products similar to a given product') and names the resource and method ('using vector similarity'). This clearly distinguishes it from sibling search tools like search_products or find_best_price, which address different intents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states a concrete use case: 'Useful for "more like this" recommendations.' This gives clear context for when to invoke the tool, though it does not explicitly name alternatives or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Five tools have near-identical v1/v2 pairs (compare_products, find_best_price, get_deals, get_product, search_products), making it very hard for an agent to know which variant to call without parsing subtle differences like required deliver_to. Although unique tools exist, the overlap dominates the surface.
All tools use a consistent snake_case verb_noun pattern such as search_products, get_deals, compare_products, and list_categories. The versioned variants consistently append _v2, giving a predictable and easy-to-infer naming convention.
13 tools is within a reasonable scope for a product-search and comparison MCP server. However, five of the tools are versioned duplicates that add little distinct capability, making the set feel slightly padded rather than optimally concise.
The tool surface covers the core buyer journey: search, compare, best-price lookup, deals, product details, similar products, categories, and catalog ingestion. Gaps are minor, such as no delete/remove catalog operation or dedicated merchant management, but primary shopping and product-discovery tasks are well supported.