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keithrawlingsbrown

refinex-mcp

get_signal_for_instance

Retrieve the best spot instance action for a specific cloud, region, and instance type. Returns a deterministic action like buy_spot or use_on_demand, optimized for autoscalers.

Instructions

Get the single best spot action for a specific cloud/region/instance type. Optimized for autoscalers that need one clear, deterministic action. Requires REFINEX_API_KEY env var.

Args: cloud: Cloud provider — "aws", "gcp", or "azure" region: AWS region, e.g. "us-east-1", "us-west-2", "eu-west-1" instance_type: EC2 instance type, e.g. "m6i.large", "c7g.xlarge", "t3.medium" fallback: What to return when no signal exists — "on_demand" (default), "wait", or "none"

Returns:

  • action: "buy_spot" | "migrate_spot" | "wait" | "use_on_demand"

  • signal{}: full signal detail for the chosen action

  • alternatives[]: other AZs for the same instance type, with their prices/confidence

Use fallback="on_demand" in CI/CD pipelines so you always get a safe default. Use fallback="wait" in cost-sensitive batch jobs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cloudYes
regionYes
instance_typeYes
fallbackNoon_demand
Behavior4/5

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

Annotations are absent, so description handles transparency fully. It discloses the required environment variable (REFINEX_API_KEY) and outlines the return structure (action, signal, alternatives). It does not mention rate limits or error conditions, but the core behavioral traits are well-covered.

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?

The description is well-structured with clear sections (Args, Returns, usage guidance) and every sentence earns its place without redundancy. It is concise yet comprehensive, covering all necessary details in a compact format.

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?

Given no output schema and 0% schema coverage, the description is fully self-contained. It explains purpose, all parameters, return format, environment variable requirement, and usage guidance. No additional information is needed for an agent to invoke the tool correctly.

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?

Schema provides no parameter descriptions (0% coverage), but the description compensates excellently. It explains each parameter: cloud provider options, region format with examples, instance type examples, and fallback options with defaults. It also clarifies the return values, adding essential meaning beyond the schema.

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?

Description clearly states the tool retrieves 'the single best spot action' for a specific cloud/region/instance type, using specific verbs and resource identifiers. It distinguishes itself from siblings like 'get_live_signal' or 'get_signals_summary' by targeting autoscalers needing a single deterministic action.

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?

Explicitly states it is optimized for autoscalers and provides detailed guidance on when to use each fallback option: 'on_demand' for CI/CD pipelines, 'wait' for cost-sensitive batch jobs. This gives clear context for tool selection.

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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