Revenue summary

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds substantial behavioral context by detailing the two distinct data sources (collected revenue vs. current MRR), the return JSON structures for each mode, and the 'basis' field that indicates which metric is being reported. It also notes that from/to only apply to group_by='month', preventing misuse.

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 longer than typical but well-structured with clear sections (main line, mode clarification, Args, return formats, examples). It front-loads the core purpose and uses bullet-style lists. Some redundancy exists with the schema parameter descriptions, but the examples and return format details justify the length.

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?

Despite having no output schema, the description fully documents the return JSON for both modes, including field names and types. It provides three usage examples covering the main group_by options. It is self-contained and sufficiently complete for an AI agent to select parameters and interpret results without external context.

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% per the provided context, so the baseline is 3. The description adds value by explaining the semantic difference between group_by values (historical invoices vs. live MRR), clarifying the default 'month', and providing concrete examples of using from/to with YYYY-MM-DD format. This goes slightly beyond the schema descriptions.

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 'Revenue broken down by month, plan, country or industry', which immediately states the verb (broken down) and resource (revenue). It clearly distinguishes between the two modes: month (collected revenue from invoices) and plan/country/industry (current MRR), differentiating this tool from sibling tools like harbor_run_query and harbor_customer_360.

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 each grouping: 'group_by='month' reads collected revenue from paid invoices' versus 'the other groupings read current MRR from live subscriptions', and explains these answer different questions. Examples like 'How much did we collect in Q2?' map to group_by='month' with date range. It does not explicitly name alternative sibling tools, but it clearly contextualizes when to invoke this tool and which parameters to choose.

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