Get monthly and yearly billing consumption totals for your account. Shows aggregated usage data from billing transactions and data transfers.
Get monthly and yearly billing consumption totals for your account. Shows aggregated usage data from billing transactions and data transfers.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds context about data sources (billing transactions and data transfers) but does not disclose additional behavioral details like time range limitations or response format.
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 text is repeated verbatim under both '# fetch_billing_consumptions' and '## When to use' headings, creating redundancy without adding information. The core message is concise, but the repetitive structure fails to earn its place.
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?
For a zero-parameter read-only tool, the description adequately explains what it returns (monthly and yearly totals, aggregated from transactions/data transfers). The lack of an output schema and low complexity mean the description is sufficient for the tool's 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?
The tool has 0 parameters, making the schema trivial. Baseline for 0 params is 4. The description has no need to explain parameter semantics, as there is nothing to document beyond the empty 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 uses a specific verb ('Get') and resource ('monthly and yearly billing consumption totals') and clarifies it shows 'aggregated usage data from billing transactions and data transfers,' distinguishing it from transaction-level tools like fetch_billing_transactions.
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?
There is a 'When to use' section, but its content merely restates the tool's purpose without providing explicit guidance on when to use this tool versus alternatives or when not to use it. The context implies use for aggregated totals, but no exclusions or sibling comparisons are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools follow a clear resource+action pattern (fetch_* for lists, get_* for single items, create/update/delete for writes), making them distinguishable. A few near-overlaps exist (fetch_billing_transactions vs fetch_wallet_transactions, draw_winners vs schedule_drawing) but the descriptions clarify the boundaries.
Tool names consistently use snake_case verb_noun, with fetch_ for list operations and get_ for single-item retrieval. Some verb variation (add_participant, count_participants, draw_winners, schedule_drawing) deviates from the dominant create/fetch/update/delete pattern but remains predictable and readable.
With 83 tools, this is far beyond a well-scoped server (typically 3-15, with 25+ considered too many). Even though the platform has broad functionality, the sheer number of tools creates significant agent confusion and selection overhead.
The sweepstakes domain is thoroughly covered: lifecycle management, participants, groups, rules, drawings, winners, calendar, notes, todos, tickets, surveys, invoices, billing, files, and entry settings. A few minor gaps exist (e.g., no general participant field update, no archive action) but the surface is remarkably complete for its scope.
