Invoice Summary Analytics

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

The description adds significant behavioral details beyond the annotations: it warns about truncation for >10,000 invoices, notes that from_date/to_date are ignored when period is specified, describes error handling ('Returns "Error: ..." if API call fails'), and outlines the return format for both JSON and Markdown. These enrich the read-only, idempotent nature already declared by annotations without contradiction.

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 with clear sections: purpose, example questions, Args, Returns, Examples, and Error Handling. It front-loads the core purpose in the first sentence. However, the Args section largely duplicates the input schema, which adds redundancy and length. The extra examples and error handling justify some length, but the duplication keeps it from a perfect score.

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?

This is a complex tool with 8 parameters and no output schema. The description compensates admirably by detailing the return structure for JSON, describing the Markdown format, explaining truncation and error handling, and providing parameter combination examples. It covers the essential behavioral and output aspects that the schema and annotations do not, making it complete enough for an agent to invoke the tool confidently.

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 coverage is 100%, so the baseline is 3. The description repeats most parameter details already in the schema, adding little new semantic meaning. It does provide useful combination examples (e.g., group_by='customer' with period='this_year') that illustrate parameter interplay, but this is more about usage context than fundamentally new parameter semantics. Thus, it meets the baseline without exceeding it.

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: 'Calculate summary statistics for invoices over a period.' It uses a specific verb and resource, and the example questions further clarify that it answers analytical queries about totals, counts, averages, and breakdowns. This distinguishes it from sibling tools like fortnox_list_invoices (which lists raw invoices) and fortnox_unpaid_report (which focuses specifically on unpaid invoices).

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 clear context on when to use this tool through example questions and examples of parameter combinations (e.g., 'Monthly revenue: period="this_month"', 'Revenue by customer this year: period="this_year", group_by="customer"'). However, it does not explicitly name alternative tools or state when not to use it, so it stops short of full exclusion guidance. Still, the context is strong enough for an agent to infer appropriate usage.

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