find_compatible_chainsFor a pendant or charm SKU, returns chains whose width fits the pendant's bail (same metal family, sensible width and price), plus other pieces the store pairs with it (same school/team or same collection). Use get_product on a returned name/URL for live price and stock.
| Name | Required | Description | Default |
|---|---|---|---|
| sku | Yes | The pendant/charm SKU, e.g. K4324 or 4Y001CU. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It explains what the tool returns, that results are filtered by metal family, width, and price sensibility, and subtly indicates the returned data should be checked via get_product for current price and stock. This is meaningful behavioral context beyond the schema.
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?
Two sentences, no filler. The first sentence delivers the core purpose and scope immediately, and the second sentence provides actionable next-step guidance. Every phrase earns its place.
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?
No output schema exists, so the description must convey the nature of the return value; it does so by listing the categories of results (fitting chains, same school/team, same collection) and pointing to get_product for live price/stock. This is sufficient for an agent to understand what the tool produces and how to use the results, though it omits details like response shape and edge 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 description coverage is 100% and the single 'sku' parameter is well-documented with an example. The description reinforces that the SKU should be a pendant or charm SKU, but adds little beyond what the schema already provides. Baseline 3 is appropriate.
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 ('returns') and clearly identifies both the input (a pendant or charm SKU) and the output (compatible chains plus recommended paired pieces). It distinguishes itself from sibling tools like get_product and search_products by focusing on compatibility and store pairings.
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 makes clear when to use the tool: for a pendant or charm SKU when looking for compatible chains or paired pieces. It also gives explicit follow-up guidance to use get_product on returned names/URLs for live price and stock, though it does not explicitly state when to prefer a sibling tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.