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 describes what data is retrieved (campaign performance metrics) and the source table, but lacks details on permissions, rate limits, response format, pagination, or error handling. For a data retrieval tool with zero annotation coverage, this is a significant gap in transparency.

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 with two sentences that directly address the tool's function and scope. It avoids unnecessary words and is front-loaded with the core purpose. However, it could be slightly more structured by explicitly separating purpose from scope.

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 complexity (5 parameters, no annotations, no output schema), the description is minimally adequate. It covers the purpose and scope but lacks details on behavioral aspects like response format, which is critical since there's no output schema. For a data retrieval tool, this leaves gaps in understanding what the tool returns.

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%, so the schema already documents all 5 parameters with descriptions and enums. The description adds no additional parameter semantics beyond what the schema provides, such as explaining relationships between parameters (e.g., how 'group_by' interacts with 'interval'). Baseline 3 is appropriate when schema does the heavy lifting.

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 Shop campaign performance metrics (ad spend, sales) from the shop_campaign_insights table.' It specifies the resource (campaign performance metrics) and source (shop_campaign_insights table). However, it doesn't explicitly differentiate from siblings like 'shopify_ad_spend' or 'shopify_sales_by_channel', which might also involve campaign or sales data.

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 some context by mentioning 'Covers Shop channel campaigns (e.g. Shop Pay campaigns)', which implies this tool is for Shop-specific campaigns rather than other types. However, it doesn't explicitly state when to use this tool versus alternatives like 'shopify_ad_spend' or 'shopify_sales_by_channel', nor does it provide exclusions or prerequisites. Usage is implied but not clearly defined.

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