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x7even

OpenCloudCosts MCP

get_spot_history

Analyze spot instance price history and stability for AWS compute types. Get per-AZ statistics, volatility ratio, and actionable recommendations to optimize spot usage.

Instructions

Get spot price history and stability analysis for a compute instance type.

Returns per-AZ spot price statistics (current, min, max, avg, sample count), overall volatility ratio, a stability label, and an actionable recommendation. Currently supported by AWS (requires credentials). GCP and Azure return not_supported.

Args: spec: PricingSpec dict with domain="compute", resource_type (instance type), region. Example: {"provider": "aws", "domain": "compute", "resource_type": "m5.xlarge", "region": "us-east-1"} hours: Lookback window in hours (default 24, max 720) availability_zone: Filter to a specific AZ, e.g. "us-east-1a". Empty = all AZs (AWS only).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
specYes
hoursNo
availability_zoneNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, description discloses provider-specific behavior and return structure. It could mention read-only nature but is otherwise transparent about supported providers and output.

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

Conciseness5/5

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

Concise and well-structured: purpose first, then returns, then provider support, then parameters with examples. Every sentence adds value.

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

Completeness5/5

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

Comprehensive given output schema exists. Summarizes returns (per-AZ stats, volatility, stability, recommendation) and provider constraints. No missing critical information.

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

Parameters5/5

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

Adds significant meaning beyond the schema: explains spec as PricingSpec dict with example, hours as lookback with default/max, availability_zone as filter. Schema coverage 0% so description is essential.

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?

The description clearly states the tool gets spot price history and stability analysis for a compute instance type, listing specific return values. This distinguishes it from sibling tools that focus on on-demand pricing or other aspects.

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

Usage Guidelines4/5

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

Provides context on provider support (AWS requires credentials, GCP/Azure return not_supported), guiding when to use. Does not explicitly contrast with alternatives but implies use for spot pricing.

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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