All provider prices for one LLM model: input/output/cache price per 1M tokens and context window per provider, cheapest first.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | model slug or name, e.g. "deepseek-v4-flash" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It transparently discloses the return data (prices per provider, per 1M tokens, context window) and ordering (cheapest first). For a read-only query tool, this is sufficient, though it does not mention authentication or rate limits.
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 sentence that covers all essential information without any extraneous words. It is front-loaded with the main purpose and details follow logically.
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's simplicity (one parameter, no output schema) and the context signals, the description adequately covers what the tool does and what it returns. However, it lacks details on error handling or case sensitivity, which would make it more robust.
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 single parameter 'model' has 100% schema coverage. The description adds meaningful context beyond the schema by specifying the format ('slug or name') and providing an example ('deepseek-v4-flash'). This helps the agent format the input correctly.
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 specifies the exact resource (LLM model prices), includes the data points (input/output/cache price per 1M tokens, context window per provider), and ordering (cheapest first). It clearly distinguishes from sibling tools which focus on GPUs and model search.
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 querying prices for a specific model but does not explicitly state when to use this tool versus alternatives or context about prerequisites. Given the sibling tools are entirely different domains, the lack of explicit guidance is acceptable but still a gap.
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: GPU rentals, model pricing, GPU history, GPU listing, model search. No overlap in purpose.
All tool names follow a consistent verb_noun snake_case pattern (e.g., cheapest_gpu, get_model_prices, list_gpus, search_models).
5 tools are well-scoped for the hosting pricing domain, covering both GPU and model pricing without being excessive.
Covers current and historical pricing for GPUs and models. Missing a tool for direct provider comparison, but core workflows are supported.
