get_all_orders

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 that store hash is automatically retrieved from environment variables, which is useful context about configuration. However, it doesn't describe critical behavioral traits like whether this is a read-only operation, pagination behavior (implied by limit/page parameters but not explained), rate limits, authentication requirements, or what happens when no filters are applied. For a tool with 19 parameters and no 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 appropriately concise with three sentences that each serve a purpose: stating the core function, explaining a key filtering use case, and providing implementation detail about store hash. It's front-loaded with the main purpose and avoids unnecessary elaboration. However, the second sentence about customer_id filtering could be more tightly integrated with the first sentence for better flow.

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 (19 parameters, no annotations, no output schema), the description provides a basic but incomplete picture. It covers the core purpose and one filtering scenario but doesn't address the tool's full behavioral context, return format, error conditions, or relationship to sibling tools. The 100% schema coverage helps with parameter understanding, but the description alone doesn't provide enough context for confident agent usage without additional inference from the schema.

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 19 parameters thoroughly. The description adds minimal value beyond the schema by mentioning customer_id filtering specifically and noting that store hash can be auto-retrieved from environment variables. This provides some contextual meaning but doesn't significantly enhance understanding beyond what's already in the parameter descriptions. The baseline of 3 is appropriate when the 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 all orders from the BigCommerce API.' It specifies the resource (orders) and the action (get), though it doesn't explicitly differentiate from sibling tools like get_all_customers or get_all_products beyond mentioning different resources. The mention of filtering by customer_id adds some specificity but doesn't fully distinguish it from potential order-related alternatives.

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 context by mentioning filtering by customer_id to get products associated with specific customers, but it doesn't explicitly state when to use this tool versus alternatives or any prerequisites. The note about store hash being automatically retrieved from environment variables offers some operational guidance, but no explicit when/when-not instructions or sibling tool comparisons are included.

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