Search Coles products by query; optionally set store ID and result limit to retrieve relevant product listings for shopping or price comparison.
Search for products at Coles.
Args:
query: The product search query.
store_id: The Coles store ID to search in.
limit: Maximum number of products to return.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| store_id | No | 0584 |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a search operation but doesn't describe what happens when no results are found, whether results are paginated, what authentication might be required, rate limits, or what the return format looks like. For a search tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
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 appropriately sized and well-structured with a clear purpose statement followed by parameter explanations. The bullet-point format for parameters is efficient. However, the first sentence could be more specific (e.g., 'Search for grocery products at Coles supermarkets'), and the parameter descriptions could be slightly more informative.
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 search tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (product names, prices, availability?), error conditions, authentication requirements, or how results are ordered. The sibling tool context isn't leveraged to provide comparative guidance, leaving significant gaps for an AI agent.
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?
The description lists all three parameters with brief explanations, but the input schema has 0% description coverage. The description adds basic semantic meaning (e.g., 'The product search query' for 'query'), but doesn't provide format details, constraints, or examples. It compensates somewhat for the schema's lack of descriptions but doesn't fully address the coverage gap, earning a baseline score.
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 clearly states the tool's purpose as 'Search for products at Coles' - a specific verb ('Search') and resource ('products at Coles'). It distinguishes from the sibling tool 'get_woolworths_products' by specifying the retailer. However, it doesn't explicitly mention what type of search this is (e.g., text-based product search vs. category browsing), which prevents a perfect score.
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 provides no guidance on when to use this tool versus alternatives. While the sibling tool name suggests a different retailer (Woolworths), the description doesn't mention this alternative or provide any context about when to choose Coles over Woolworths. There's no information about prerequisites, constraints, or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.