Return the dated $/M-token price history (input + output) for one model. This is AI API Radar's exclusive time-series — useful for spotting price-war cuts and trend.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | Model slug, e.g. 'anthropic--claude-opus-4-x' or just the id. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It only mentions the tool is read-only by nature (returning data) but lacks details on error cases, rate limits, or any side effects. The agent cannot infer safety or constraints beyond the basic operation.
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 two sentences, front-loading the core action and adding a use-case hint. Every sentence adds value without unnecessary fluff.
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 1 parameter, no output schema, and no annotations, the description covers the basics but lacks details on return format, pagination, or ordering. It mentions input+output prices but does not specify data types or structure, leaving gaps for an agent to determine how to use the response.
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 100% (the model parameter has a description with an example). The description adds minimal value beyond the schema, simply reiterating the parameter's purpose. Baseline 3 is appropriate as the schema already carries the load.
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 verb 'Return' and the resource 'dated $/M-token price history (input + output) for one model.' It also mentions its unique aspect as 'AI API Radar's exclusive time-series,' which differentiates it from sibling tools like cheapest_model or price_change_alerts.
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 implies usage for 'spotting price-war cuts and trend,' providing some context on when to use. However, it does not explicitly state when not to use or specify alternatives, leaving the agent to infer from sibling names.
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 targets a distinct aspect: cheapest model search, price change alerts, price history, and provider status. No overlap in purpose.
All tool names use lowercase with underscores and follow a predictable noun_noun or adjective_noun pattern. Naming is clear and consistent.
With 4 tools, the server is well-scoped for its purpose of monitoring AI API prices and status. Each tool is justified.
While core functionalities are covered, the server lacks a tool to list available providers or models, which is a notable gap for initial discovery.