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Last Price

by ai.last-price
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simulate_agent_cut

Simulate how much of a sale to offer an agent platform (a shopping agent or agent marketplace that charges sellers a cut) if it ranks sellers partly by the cut they pay. No platform publishes such a ranking, so this is a simulation under your assumption (visibility_per_point_pct: percent more channel sales per extra point of cut), also run at half, double and zero of it. Returns the profit at each cut, the recommended cut, the cut with the smallest worst case, the break-even, and a channel rate ready for PUT /api/channel-rates. Changes nothing. Answers 404 until the cut advisor is switched on for your workspace. Needs a Last Price credential with routing read scope. Needs a claimed workspace: a sandbox key from self-registration is refused (403) until a person claims the workspace.

Input Schema

NameRequiredDescriptionDefault
priceYesYour own price before any channel cut.
channelNoThe channel name, as you save it in channel rates (e.g. instinct).
currencyNoISO 4217 code of price and unit_cost, echoed back.
step_pctNoGap between the cuts tried. Defaults to 0.5.
unit_costYesWhat one unit costs you, same currency.
base_unitsYesUnits sold through the channel per period at the current cut.
max_cut_pctNoHighest cut to try. Defaults to 30 (or the current cut, if higher).
min_cut_pctNoLowest cut to try. Defaults to 2.5 (or the current cut, if lower).
passed_on_pctNoHow much of the cut is added to the price, in percent. Defaults to 100.
current_cut_pctYesThe cut the channel takes today, in percent.
price_elasticityNoHow channel buyers respond to price. Defaults to -1.5.
visibility_per_point_pctNoYour assumption: percent more channel sales per extra point of cut. Defaults to 2.
max_visibility_change_pctNoMost ranking can move channel sales, in percent either way. Defaults to 100.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral burden and does so: it states 'Changes nothing', lists the returned artifacts, discloses the 404 gating on the cut advisor, the credential scope, and the 403 refusal for unclaimed sandbox workspaces. These are precisely the operational traits an agent needs before calling.

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 purpose and model lead, followed by return values and prerequisites, so it is front-loaded and every sentence carries information. It is dense and somewhat run-on, but not padded; slight tightening would earn a 5.

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?

For a 13-parameter simulation with no output schema and no annotations, the description covers side effects, preconditions, return contents, and assumption handling. An agent has enough to decide whether it can call the tool and how to interpret results without additional fields.

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 the schema supplies parameter meanings. The description still adds value by explaining that visibility_per_point_pct is a user assumption and that the simulation is run at half, double, and zero of it, which is interpretive context absent from the schema field text.

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 names a specific verb and resource ('Simulate how much of a sale to offer an agent platform') and immediately explains the model it uses, the sensitivity analysis, and what it returns. An agent can distinguish it from settlement tools like set_channel_rates or analytical siblings like get_learned_elasticity_curve without opening the schema.

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?

It gives strong preconditions (cut advisor enabled, routing read scope, claimed workspace) and notes the output is 'ready for PUT /api/channel-rates', implying use before committing a channel rate. It stops short of naming an alternative tool or stating explicit when-not-to-use conditions, which keeps it below a 5.

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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