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mikki321

night-margin-mcp

by mikki321

Rank where money moves and weigh the options (read-only)

margin_copilot

Identifies nights priced below your cost floor, ranks them by below-floor exposure, and recommends guarding the floor or raising minimum stay to protect profit margins.

Instructions

Margin Copilot: reads the same figures as propose_decisions, groups the gap nights priced below your cost floor into per-property money-moves, ranks them by below-floor exposure (biggest first), and weighs the options for each — Hold, Guard the floor (recommended), and where it applies, raise the minimum stay — with one marked recommended. The only euro figures are computed (below-floor exposure, floor levels); it does NOT invent a '30-day net' or option deltas that would require guessing fill probability, and it never claims an unsold night as a gain. Read-only — it never writes prices or state. Staging a recommended move routes to propose_decisions → apply_decision, where every write is previewed and needs an explicit confirm. Requires WHEELHOUSE_API_KEY (uses live price recommendations).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoLatest night to scan, YYYY-MM-DD. Optional — defaults to the next 30 days.
fromNoEarliest night to scan, YYYY-MM-DD. Optional — defaults to the next 30 days (the horizon Wheelhouse price recommendations cover).
riskNoRisk preset for the cost floor (margin multiplier). Defaults to 'recommended'. Same presets as propose_decisions.
Behavior5/5

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

With no annotations provided, the description fully discloses behavior: it is read-only (never writes prices or state), does not invent figures like a '30-day net', and requires an API key. It also clarifies what the tool does not do (claim unsold night as gain). This level of detail exceeds requirements.

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

Conciseness4/5

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

The description is well-structured and front-loaded, but somewhat verbose. Every sentence adds value, but the paragraph could be slightly more concise. Nonetheless, it is clear and organized.

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, the description sufficiently explains what the tool returns (ranked list, options with recommended one). It also covers the workflow context (staging moves leads to propose_decisions/apply_decision). The parameter count is low and all are optional, so completeness is high.

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?

The input schema has 100% coverage with descriptions for each parameter. The description adds additional context: defaults for from/to (next 30 days), and risk presets are same as propose_decisions. This adds value 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?

The description clearly states the tool reads figures, groups gap nights, ranks by below-floor exposure, and weighs options (Hold, Guard floor, raise minimum stay). It explicitly distinguishes itself from propose_decisions by noting that it reads the same figures but does not write, and that staging a move routes to propose_decisions. The verb 'reads' and resource 'margin copilot' are specific, and sibling differentiation is clear.

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?

The description provides explicit when-to-use guidance: it is read-only and should be used to inspect margin opportunities. It also tells the agent when not to use it (for actual writes, use propose_decisions → apply_decision) and how to route actions. It mentions the requirement for WHEELHOUSE_API_KEY. This is complete usage guidance.

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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