mcp-livedatapro

getCloudServerPricing

Compare live hourly and monthly VPS, dedicated CPU, and GPU (H100/A100) server spot/on-demand prices across AWS, GCP, Azure, and Hetzner. Use when comparing live on-demand and spot hourly/monthly USD prices for cloud VPS, dedicated CPU, and AI GPU instances (H100, A100, RTX4090) across AWS, GCP, Azure, and Hetzner. Do not use for per-token LLM inference API pricing (use getAiModelPricing). Returns matching SKUs sorted by lowest hourly cost.

Input Schema

Name	Required	Description	Default
`min_vcpu`	No	Minimum required virtual CPU cores (1 to 256, defaults to 4).
`min_ram_gb`	No	Minimum required system RAM in gigabytes (1 to 2048, defaults to 16).
`gpu_required`	No	Optional GPU accelerator model filter (defaults to 'none' for CPU-only instances).	none

Schema Changelog

Changes observed during successful MCP inspections.

Changed12 schema fields changedOct 24, 2026
- addedInput schema / additionalProperties
```
Added value: +false
```
- addedInput schema / properties / gpu_required / default
```
Added value: +"none"
```
- addedInput schema / properties / gpu_required / enum
```
Added value: +[
+  "none",
+  "H100",
+  "A100",
+  "L40S",
+  "RTX4090",
+  "T4"
+]
```
- addedInput schema / properties / gpu_required / examples
```
Added value: +[
+  "H100"
+]
```
- addedInput schema / properties / min_ram_gb / default
```
Added value: +16
```
- addedInput schema / properties / min_ram_gb / examples
```
Added value: +[
+  32
+]
```
- addedInput schema / properties / min_ram_gb / maximum
```
Added value: +2048
```
- addedInput schema / properties / min_ram_gb / minimum
```
Added value: +1
```
- addedInput schema / properties / min_vcpu / default
```
Added value: +4
```
- addedInput schema / properties / min_vcpu / examples
```
Added value: +[
+  8
+]
```
- addedInput schema / properties / min_vcpu / maximum
```
Added value: +256
```
- addedInput schema / properties / min_vcpu / minimum
```
Added value: +1
```

Changed3 schema fields changedOct 24, 2026

changedInput schema / properties / gpu_required / description

Previous value: -"Optional GPU filter (e.g., H100, A100, RTX4090, none)"New value: +"Optional GPU accelerator model filter (defaults to 'none' for CPU-only instances)."

changedInput schema / properties / min_ram_gb / description

Previous value: -"Minimum required RAM in GB"New value: +"Minimum required system RAM in gigabytes (1 to 2048, defaults to 16)."

changedInput schema / properties / min_vcpu / description

Previous value: -"Minimum required virtual CPUs"New value: +"Minimum required virtual CPU cores (1 to 256, defaults to 4)."

First observedOct 21, 2026

TDQS

A4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must carry the full burden. It discloses freshness ('live'), currency (USD), and result ordering ('sorted by lowest hourly cost'), which is useful. It says nothing about rate limits, auth, pagination, coverage gaps (e.g. regions, spot availability windows), or what fields come back beyond the sort key.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose, but the second sentence largely restates the first — the provider list and instance classes are repeated nearly verbatim, making the definition roughly twice as long as needed. The exclusion and sort-order notes do earn their place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, and the description at least characterizes the return (matching SKUs ordered by lowest hourly cost), plus scope and the key exclusion. It would be stronger with a note on what each SKU record contains and whether all providers are guaranteed to be represented.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all three filters (min_vcpu, min_ram_gb, gpu_required) are already documented with ranges and defaults. The description only echoes the GPU model names and omits some enum values (L40S, T4), adding no semantics beyond the schema; baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a concrete action (compare live hourly/monthly prices) with the exact resource classes (VPS, dedicated CPU, GPU H100/A100) and provider set (AWS, GCP, Azure, Hetzner). It also explicitly names the sibling it is not for (getAiModelPricing), so the agent can differentiate without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Contains an explicit 'Use when...' clause listing the qualifying scenario and a 'Do not use for per-token LLM inference API pricing (use getAiModelPricing)' exclusion that routes the agent to the correct alternative. Both when-to-use and when-not-to-use are covered.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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