Shopify MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool is 'useful for trends and comparisons,' which hints at its analytical nature, but fails to disclose critical behaviors like whether it requires authentication, has rate limits, returns paginated results, or what happens with invalid inputs. For a data retrieval tool with zero annotation coverage, this is a significant gap.

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 highly concise and well-structured, consisting of two sentences that efficiently convey the core purpose and utility. Every sentence earns its place: the first states what the tool does, and the second adds context on its usefulness. There is no wasted verbiage or redundancy.

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?

Given the tool's complexity (5 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits, output format, or error handling. Without annotations or an output schema, the agent might struggle with how to interpret results or handle edge cases, making this incomplete for robust usage.

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 does not add any parameter-specific information beyond what the input schema provides. Since schema description coverage is 100%, the schema already documents all parameters (e.g., 'since', 'until', 'interval', 'compare', 'metrics') with descriptions and examples. The baseline score of 3 is appropriate as the schema does the heavy lifting, and the description doesn't compensate with additional semantics.

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: 'Get sales metrics over time as a time series.' It specifies the verb ('Get'), resource ('sales metrics'), and output format ('time series'). However, it doesn't explicitly differentiate from siblings like 'shopify_sales_summary' or 'shopify_sales_by_product', which might offer similar sales data in different formats or scopes.

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 implied usage guidance with 'Useful for trends and comparisons,' suggesting when to use this tool. However, it lacks explicit alternatives or exclusions, such as when to choose this over 'shopify_sales_summary' for aggregated data or 'shopify_sales_by_product' for product-level details. No clear when-not-to-use scenarios are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.