Search LLM models by name. Returns matching models with provider count and the cheapest input/output price per 1M tokens.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | model name or fragment, e.g. "deepseek" or "llama 70b" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the key behavioral traits: it returns matching models, provider count, and cheapest prices per 1M tokens. Since no annotations are present, this description carries the full burden and does so adequately, though it omits details like pagination 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?
Two sentences, each serving a clear purpose: action and return value. No wasted words, highly efficient.
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 only one required parameter and no output schema, the description sufficiently states what the tool does and returns. However, it does not mention potential limitations like pagination, which would add minor completeness.
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 input schema covers the single parameter 'query' with a description. The tool's description adds context by mentioning the search is by name and provides examples ('deepseek', 'llama 70b'), adding value beyond the 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 clearly states the action ('Search LLM models by name') and the resources involved (LLM models). It distinguishes from sibling tools like cheapest_gpu and get_model_prices which focus on GPU or pricing data.
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 searching models by name, but does not explicitly state when to use this tool versus alternatives like cheapest_gpu or get_model_prices. No when-not-to-use or exclusion criteria provided.
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.
