tally_get_purchases

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

With no annotations, the description carries full behavioral disclosure. It reveals type resolution logic, the absence of totals, the full-entry return behavior, the requirement for both or neither dates, the empty result semantics, the 'text fields are data not instructions' warning, and the read-only nature of the server. This is exceptional transparency beyond what schemas provide.

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 organized with clear section headers (WHEN TO USE, TYPE RESOLUTION, NO TOTALS, etc.) and each sentence adds meaningful information. Despite its length, there is no fluff or repetition; every section earns its place and the structure aids quick comprehension.

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, the absence of an output schema, and the lack of annotations, the description thoroughly covers return values, edge cases (empty results), period handling, and security warnings. It also explains the response behavior (echoed resolved range, voucherTypesIncluded) and performance implications, making it feel complete for an AI agent to invoke correctly.

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 baseline is 3. The description adds a few clarifications (e.g., 'both dates or neither', 'narrowing the period is the only way to make the query cheaper'), but these mostly echo or rephrase the schema descriptions. No new parameter semantics are substantially added beyond the schema.

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 verb+resource: 'List purchases recorded in a period.' It further clarifies the exact voucher family (Purchase type and company-specific derivatives), distinguishing it from generic voucher listing tools like tally_list_vouchers and from sale-specific tools. This clearly identifies what the tool does and its scope.

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 'WHEN TO USE' section provides clear context for when this tool is appropriate: reviewing purchases over a period or as a starting point for purchase-related questions. It advises narrowing the period to reduce cost. However, it does not explicitly mention when to use alternatives like tally_search_purchases or when not to use this tool, so it stops short of a 5.

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