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mikki321

night-margin-mcp

by mikki321

Propose pricing decisions (gap night floors)

propose_decisions

Finds upcoming gap nights where current price is below cost floor and proposes fixing them at the floor to prevent selling at a loss.

Instructions

Proposes concrete pricing decisions in euros: finds upcoming gap nights where the current price recommendation is below your cost floor (turnover + travel + MIN_MARGIN, adjustable with the risk preset) and proposes fixing those nights at the floor so they can't sell below cost. Proposals are saved to the local decision log (NM_STATE_DIR, default ~/.night-margin). Window defaults to the next 30 days. Proposing never changes prices — prices change only when a decision is applied with apply_decision and explicit confirmation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoWindow end (exclusive), YYYY-MM-DD (optional — defaults to 30 days from the start)
fromNoWindow start, YYYY-MM-DD (optional — defaults to today; decisions apply to future nights)
riskNoRisk preset for the gap-night floor's minimum margin (Wheelhouse's own CON/REC/AGG language): conservative doubles MIN_MARGIN, recommended keeps it as configured (default), aggressive uses 40% of it.
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that proposals are saved to a local decision log, never change prices, and that the risk preset affects the minimum margin. It does not detail authentication or error handling, but covers the core behavior well.

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 a single paragraph of around 100 words, front-loaded with the main purpose. It is concise, but could be slightly more structured with bullet points or separate sentences for each detail. Still, it is well within acceptable length.

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 3 optional parameters, no output schema, and no annotations, the description is complete. It covers the tool's operation (finds gap nights, proposes fixes), defaults (window, risk), and side effects (saves to log, no price changes). No important aspects are missing.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining default values for 'from' and 'to' (today and 30 days) and the meaning of the 'risk' enum (conservative doubles MIN_MARGIN, recommended keeps it, aggressive uses 40%). This goes beyond the schema's brief descriptions.

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's purpose: proposing pricing decisions for gap nights by identifying those below cost floor and suggesting fixes. It uses specific verb+resource ('propose decisions') and distinguishes itself from sibling tools like apply_decision by clarifying that proposals do not change prices.

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?

The description explains when to use the tool (for gap nights below cost floor) and contrasts it with apply_decision, which actually applies changes. It mentions defaults (30-day window) and adjustable risk presets but does not explicitly list all alternative tools or specify when not to use it.

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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