list_gpu_pricesOne entry per GPU model with the current cheapest live rental price across the whole market (RunPod, Vast.ai, Lambda, hyperscalers and more). No key required. Use this to compare GPU prices.
| Name | Required | Description | Default |
|---|---|---|---|
| tier | No | Filter by tier. | |
| vendor | No | Filter by GPU vendor. |
| Name | Required | Description | Default |
|---|---|---|---|
| gpus | Yes | ||
| count | Yes | ||
| stale | No | ||
| updated_at | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations, the description meaningfully adds behavioral context: results are live rental prices, there is one entry per GPU model, and no authentication key is required. These details help the agent predict behavior and call prerequisites without contradicting the readOnly/openWorld/idempotent annotations.
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 two sentences with no wasted words. It front-loads the core behavior and market scope, then adds auth and usage guidance. Every clause contributes actionable information.
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 simple, read-only list tool with a full input schema, an output schema, and rich annotations, the description covers the essential operational details: market coverage, live pricing, per-model granularity, and auth-free access. Nothing critical is missing for an agent to select and invoke the tool correctly.
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%, with both parameters (tier and vendor) described in the schema itself. The description does not add parameter-level detail, but according to the baseline this is acceptable since the schema carries the semantic burden.
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 a specific verb (list), a precise resource (GPU models with current cheapest live rental price), and a well-defined market scope. It also includes an explicit usage directive ('Use this to compare GPU prices') that makes the tool's intent unambiguous.
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 provides clear context for when to use the tool: when comparing GPU prices across the market. It notes that no key is required, which lowers the barrier for use. It does not explicitly name alternatives or exclusions relative to the sibling tool match_workload, so it stops just short of a 5.
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.
The two tools have clearly distinct purposes: list_gpu_prices provides raw market data, while match_workload turns a workload description into a recommendation. There is no functional overlap between them.
Both tool names follow the same verb_noun snake_case pattern: list_gpu_prices and match_workload. The naming is consistent, descriptive, and predictable.
Two tools is on the thin side, but each tool earns its place for the server's narrow price-comparison and workload-matching purpose. The count feels minimal rather than excessive, so it lands in the borderline range.
For the stated purpose of comparing GPU prices and recommending the best match for a workload, the surface is complete: one tool exposes the live market, and the other provides the routed decision. No obvious dead ends or missing critical operations.