get_bonus_ledger

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions ordering and entry types but does not describe whether the operation is read-only, requires authentication, returns paginated results, or what fields are included. While adequate for a simple list, it leaves some behavior implicit.

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 two sentences long, front-loads the main purpose, and adds just enough context to differentiate from siblings. Every word earns its place with no redundancy.

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 has zero parameters and no output schema, the description sufficiently covers what the tool does and why one would use it. It answers the likely user question. However, it omits any mention of potential filtering or the exact return format, which could be useful but is not critical for such a simple tool.

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 has zero parameters, so schema coverage is 100%. The description does not need to add parameter details. Per guidelines, 0 parameters yields a baseline of 4.

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 lists bonus-credit ledger entries, specifies ordering (newest first), and distinguishes between positive (campaign grants) and negative (promo consumption) entries. It also explicitly differentiates from the sibling tool get_credit_ledger, which tracks real credits.

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 a clear use case ('explain where did my bonus go?') and explicitly names the alternative get_credit_ledger for real credit tracking, guiding when to use which. However, it does not explicitly list when not to use the tool or conditions that might make get_bonus_balance more appropriate.

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