Gives an estimate of how much GPU usage might cost for most Nvidia GPUs while renting from a cloud provider.
Scored across 2 tools
The two tools have completely distinct purposes: one lists available GPU tiers and pricing, the other estimates job costs. There is no overlap or ambiguity in their intended use.
Both tools follow the same verb_noun pattern in snake_case: estimate-job-cost and list-skus. The naming style is fully consistent and predictable.
With only 2 tools, the server feels thin even for a narrowly scoped cost estimator. While each tool is essential, the count is at the borderline where the surface may be insufficient for more complex workflows.
The core workflow of listing SKUs and estimating costs is covered well, but there is no explicit way to view workload presets or compare multiple SKUs side-by-side. These are minor gaps that agents can work around with the current tools.
Estimate on-demand cost for a GPU job across Enverge tiers (e.g. DGX Spark vs B300) using the pricing catalogue. Pass hours, or a workload preset for an illustrative default. Always re-run before launch; returns a portal launch_url.
| Name | Required | Description | Default |
|---|---|---|---|
| skus | No | Subset of tiers to compare; defaults to the full pricing catalogue. | |
| hours | No | Wall-clock GPU hours for the job. Prefer user-supplied hours over presets. | |
| quantity | No | Number of GPUs / nodes (default 1). | |
| workload | No | Illustrative preset when hours are unknown: sft-7b-small, sft-70b, inference-day. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It does disclose that the tool returns a 'portal launch_url' and warns to 'Always re-run before launch', implying dynamic pricing. However, it does not explicitly state that the tool is read-only or has no side effects (though 'estimate' strongly implies it). It also omits any mention of authentication or rate limits, which are typically not essential for a cost estimator. The provided behavioral traits are useful but not exhaustive.
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 three concise sentences with zero filler. It front-loads the purpose, then gives usage guidance, and ends with a critical reminder and return value. Every sentence earns its place and there is no redundancy.
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?
The description covers the essential context: what the tool does, how to invoke it (hours vs workload), when to re-run, and what it returns (launch_url). The parameters are fully described in the schema. However, it does not specify the behavior when neither hours nor workload is provided, and it does not mention the sibling list-skus as a related tool. These are minor gaps given the tool's simplicity and the schema's 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?
Schema description coverage is 100%, so the schema already documents each parameter's meaning and constraints. The description adds the note to 'Pass hours, or a workload preset', which mirrors the schema's guidance that hours should be preferred over presets. This is marginal added value over the schema, so the baseline of 3 is appropriate.
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 opens with a specific verb (estimate), a clear resource (on-demand cost for a GPU job), and a defined scope (across Enverge tiers, e.g. DGX Spark vs B300). It also mentions the pricing catalogue as the data source. This clearly distinguishes it from the sibling list-skus, which would only enumerate tiers, not compute costs.
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 explicitly instructs to 'Pass hours, or a workload preset' and to 'Always re-run before launch', giving clear timing guidance. However, it does not name the sibling list-skus as an alternative or state when not to use this tool, so it lacks explicit exclusions. Still, the context is clear enough for an agent to select this tool over list-skus for cost estimation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List Enverge GPU tiers from the pricing catalogue, with hourly rates and access type (self_serve vs request_capacity). Use before comparing clouds or recommending a rental.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does disclose the output contents (hourly rates, access type) and implies a read-only list operation, but it does not mention ordering, pagination, or whether all tiers are returned.
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 with no filler. The core action is front-loaded, and the usage guidance appears in the second sentence. Every clause adds value.
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 no-parameter list tool, the description covers the operation, source catalog, returned fields, and primary use case. A note on output limits or ordering would make it fully complete, but nothing critical is missing for correct invocation.
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 tool has zero parameters and an empty schema, so there is no parameter burden; the baseline is 4. The description adds no parameter-level details, but none are needed since schema coverage is 100%.
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 names the exact verb and resource ('List Enverge GPU tiers'), identifies the source ('pricing catalogue'), and specifies the included data points (hourly rates, access type). This clearly distinguishes it from the sibling estimate-job-cost, which computes costs rather than lists catalog items.
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 explicitly states when to use it: 'Use before comparing clouds or recommending a rental.' It does not list exclusions or alternatives, but with a single sibling the usage context is sufficiently clear and not misleading.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
estimate-job-costlist-skus
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