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get_shipping_stats

Analyze shipping performance by viewing shipment counts, cost breakdowns, carrier usage, tracking status, and daily trends to optimize logistics operations.

Instructions

Get shipping statistics and analytics. View shipment counts, cost breakdowns, carrier usage, tracking status, and daily trends.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
integration_keyNoShipi integration key
periodNoPeriod: today, week, month, year, allmonth
date_fromNoCustom start date (YYYY-MM-DD)
date_toNoCustom end date (YYYY-MM-DD)
Behavior2/5

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 states the tool 'gets' and 'views' data, implying a read-only operation, but fails to mention critical behaviors such as authentication needs (implied by integration_key), rate limits, response format, or whether it aggregates real-time vs. historical data. This leaves significant gaps for a tool with four parameters and no output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is highly concise and front-loaded, using a single sentence that efficiently lists key data points without redundancy. Every phrase ('shipping statistics and analytics', 'view shipment counts...') directly supports the tool's purpose, with zero wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (4 parameters, no annotations, no output schema), the description is incomplete. It lacks details on authentication, response structure, error handling, and how parameters interact (e.g., period vs. custom dates). While concise, it does not adequately compensate for the missing structured data, leaving the agent under-informed for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all four parameters (integration_key, period, date_from, date_to). The description adds no parameter-specific semantics beyond implying a temporal focus through terms like 'daily trends', which aligns with the period/date parameters but does not provide additional syntax or format details. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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 ('get', 'view') and resources ('shipping statistics and analytics'), listing concrete data points like shipment counts, cost breakdowns, and carrier usage. However, it does not explicitly differentiate this tool from potential sibling analytics tools (e.g., get_account_info might also provide stats), 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.

Usage Guidelines2/5

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. It does not mention prerequisites (e.g., requiring an integration_key), exclusions, or comparisons to sibling tools like get_account_info or search_shipments, leaving the agent to infer usage context solely from the tool name and parameters.

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

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