Shopify MCP Server

Does 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 mentions the tool is for 'advanced analytics' and lists available tables, which adds useful context about scope. However, it doesn't disclose important behavioral traits like authentication requirements, rate limits, query execution time, error handling, or what happens with invalid queries. For a custom query tool with zero annotation coverage, this leaves significant gaps.

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 perfectly concise with two sentences that each earn their place: the first states the purpose and usage guidelines, the second provides essential context about available tables. No wasted words, and information is front-loaded effectively.

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 (custom query execution with no output schema) and zero annotation coverage, the description provides good purpose and usage context but lacks important behavioral details. It doesn't explain what the tool returns, error conditions, or execution constraints. For a powerful analytics tool, more completeness would be helpful despite the concise structure.

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 the single 'query' parameter thoroughly. The description adds value by listing available tables (sales, orders, customers, sessions, shop_campaign_insights) which provides semantic context for what can be queried, going beyond the schema's technical requirements. This compensates well despite the high schema coverage.

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 with specific verbs ('Run a custom ShopifyQL query') and resources ('advanced analytics'), explicitly distinguishing it from sibling tools by mentioning 'when the pre-built tools don't cover your specific metric needs.' It also lists available tables, providing concrete scope.

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 explicit guidance on when to use this tool ('when the pre-built tools don't cover your specific metric needs'), effectively distinguishing it from the many sibling tools listed. It clearly positions this as an alternative for advanced or specific analytics not covered by pre-built options.

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