Show this account's metered generation spend this period and its billing position: the usage cap, what has accrued toward it, and whether generation is waiting on a payment. Use it to report costs back to your human.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds context about showing billing position and payment dependency, which supplements the structured data 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?
Two succinct sentences deliver the full purpose and usage guidance with no waste. The key elements are front-loaded, making it easy for an agent to quickly understand the tool.
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 with no output schema, the description fully satisfies the need by detailing what information is displayed. No gaps remain for an agent to infer.
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?
There are no parameters, and schema coverage is 100%. The description does not need to explain parameters, and it adds no irrelevant detail. A baseline score of 4 is appropriate for zero-parameter tools.
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 explicitly states the tool shows 'metered generation spend this period and its billing position', including specific items like usage cap, accrued amount, and payment status. This provides a clear verb-resource pairing and distinguishes from sibling tools like 'get_playbook' or 'assemble_status'.
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 ends with 'Use it to report costs back to your human.', offering a clear use case. While it does not explicitly list when not to use it or compare to alternatives, the context of sibling tools makes the purpose distinct enough.
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.
Each tool has a clear, distinct purpose. The status checkers (assemble_status, clips_status) are differentiated by the job type they monitor. Memory tools (index, note, recall) are distinct operations. Competitor ad tools (search, get ads, get details) and video generation pipeline (avatar, clips, assembly) are well-separated.
All tools use consistent snake_case with a verb_noun or noun_verb pattern (e.g., assemble_video, get_competitor_ads, memory_index). No mixing of conventions, making the tool names predictable and easy to understand.
With 17 tools, the server covers account management, memory, competitor research, and video generation without being overwhelming. The count is well-scoped for its functionality, neither too sparse nor too heavy.
The toolset covers the core workflows: account setup (sign_up, upgrade, whoami), memory (CRUD), competitor analysis (search, get ads, details), and video ad creation (avatar, clips, assembly). Minor gaps like lacking a delete memory tool are acceptable given the domain focus.
