Record an AI API call and get real-time usage counters plus warnings if approaching limits.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | ||
| api_key | Yes | Your tracker API key | |
| provider | Yes | ||
| latency_ms | No | ||
| tokens_used | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full behavioral burden. It only mentions recording and getting counters/warnings, but does not disclose side effects (e.g., persistent record creation), authentication requirements, error behavior, or rate limits. This leaves significant ambiguity for an agent.
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 a single, concise sentence that front-loads the primary action and outcome. No filler words or redundant information. It earns a high score for efficiency.
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 5 parameters, no output schema, and no annotations, the description is insufficient. It does not explain the return shape of 'usage counters plus warnings', nor provide prerequisites or parameter usage guidance. An agent would need to infer too much from the schema alone.
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 only 20% (only api_key has a description). The tool description does not compensate for the other parameters (model, provider, latency_ms, tokens_used) by explaining their roles or relationships. It merely says 'AI API call', which is too vague to substitute for schema documentation.
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 ('Record') and resource ('an AI API call'), and clearly distinguishes itself from sibling tools that are read-only (get_forecast, get_provider_limits, list_providers) by emphasizing action and real-time feedback. It states the primary function and outcome in one sentence.
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 context is clear: use this tool when you need to record an AI API call and receive usage feedback. However, it does not explicitly mention alternatives or when not to use it, relying on the sibling names to imply differentiation. This is acceptable but lacks explicit exclusions.
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 clearly distinct purpose: listing providers, fetching limits, recording usage, and forecasting. No overlap exists between them.
All tool names follow a consistent verb_noun snake_case pattern (list_providers, get_provider_limits, track_usage, get_forecast).
With 4 tools, the server is well-scoped for its purpose. Each tool addresses a core aspect of rate limit tracking without unnecessary bloat.
The surface covers discovery, limit specs, usage recording, and forecasting. A minor gap is the lack of historical usage queries or reset functionality, but the core workflow is complete.