ai_usage_budget_status

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

With no annotations, the description carries the full burden. It discloses reading from specific tables, summing logs, and the special case of limit_eur=0. It does not mention side effects, but the read-only nature is clear. The dependency on l10n_bg_ai_invoice_glue is noted.

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 concise, with three sentences that front-load the main purpose and provide key details without 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 no output schema and a simple status tool, the description is fairly complete: it covers source, computed fields, and a special case. However, lack of parameter explanation is a gap.

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 0%, so the description must compensate. However, it does not explain the 'connection' or 'tenant_code' parameters. The logic explanation does not clarify how these parameters affect behavior.

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 it reads budget and usage logs and returns spent, limit, remaining, and percentage used. It distinguishes itself from sibling tools like ai_usage_log_query by focusing on status vs. raw log queries, but does not explicitly name alternatives.

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?

No explicit guidance on when to use this tool versus siblings like ai_usage_log_query or ai_usage_log_stats. The context provides sibling names but the description does not offer selection criteria.

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