Full customer picture

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

Although annotations already declare readOnlyHint=true and idempotentHint=true, the description adds valuable behavioral context: it specifies the exact return structure in JSON, indicates that an error names the id if the customer does not exist, and notes that it aggregates data from multiple sources. This goes beyond the annotations and helps the agent understand side effects (none) and failure modes, which is particularly useful given the absence of an 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.

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

The description is well-organized and front-loaded: a one-sentence summary, usage guidance, args, return schema, example, and error behavior. Every section serves a purpose. The detailed JSON return structure is necessary because there is no output schema, and the example clarifies the tool's role in a broader workflow. Nothing is wasted or redundant.

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?

For a tool with no output schema and moderate complexity (aggregating five data areas), the description is exceptionally complete. It covers the purpose, usage context, input format, full return structure, an example workflow, and error behavior. It also integrates gracefully with sibling tools by referencing harbor_find_customer. There are no significant gaps.

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 input schema already fully describes the single parameter customer_id with a pattern and explicit instruction to use harbor_find_customer if only a name is available. The description's 'Args' section repeats the same information ('exact id, format cus_0042') without adding new meaning. With 100% schema coverage, the baseline is 3, and the description adds no additional parameter 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 opens with a specific and comprehensive statement: 'Everything about one customer in a single call: profile, subscription, recent invoices, open tickets and 90-day usage totals.' This clearly identifies the tool as an aggregation endpoint and differentiates it from siblings like harbor_find_customer (which locates IDs) and harbor_revenue_summary (which focuses on revenue). The verb 'get' is implied by 'Everything about one customer', making the purpose unmistakable.

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?

Explicit guidance is provided: 'Use this instead of four separate queries when a human asks about an account.' The example 'Why is Kestrel Robotics unhappy?' -> find_customer, then this tool illustrates the intended workflow and contrasts with harbor_find_customer. This clearly communicates when to use the tool and what to do before calling it, fulfilling the dimension fully.

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