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opendata-radar-pro

simulate_public_auction_margin

Free utility tool: Calculates South Korean statutory acquisition tax, loan interest, all-in cost, equity ROE, and recommended max bid cap for public asset sales.

Input Schema

NameRequiredDescriptionDefault
categoryNoRESIDENTIAL (1.1%~3.3% tax) or COMMERCIAL_LAND (4.6% tax)
bidManwonYesPlanned bid price in Manwon (e.g. 42000 for 4.2억원)
ltvPercentNoLoan-to-value % (default 65)
propertyTitleNo
appraisalManwonYesAppraisal price in Manwon (e.g. 80000 for 8억원)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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

No annotations exist, so the description carries the full burden. 'Free utility tool' signals no auth or cost barrier and the wording implies a pure simulation with no external side effects, but it never confirms that no live auction data is fetched or whether results are deterministic. For a no-annotation tool this leaves meaningful gaps.

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?

A single front-loaded sentence with no filler, and the output list is the most useful part placed early. The 'Free utility tool:' prefix is slightly marketing-flavored but does convey cost/auth information.

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

Completeness4/5

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

There is no output schema, and the description compensates by enumerating the five computed values, which is exactly what an agent needs to judge relevance. The residual gaps are the undocumented propertyTitle parameter and the missing units/format of returned figures.

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

Parameters3/5

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

Schema coverage is 80%, so the baseline is 3. The description names the computed outputs but adds nothing about input semantics such as units, whether category defaults to RESIDENTIAL, or what propertyTitle is used for; that text stays entirely in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Uses a specific verb ('Calculates') and enumerates the exact outputs: acquisition tax, loan interest, all-in cost, equity ROE, and max bid cap, scoped to South Korean public asset sales. An agent can tell it is a computation tool rather than the data-retrieval siblings like query_kamco_public_auctions, though no sibling is named explicitly.

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

Usage Guidelines3/5

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

Usage is only implied: an agent must infer this is for evaluating a planned bid on a public auction. There is no statement of when to use this versus query_kamco_public_auctions or query_public_procurement_bids, and no prerequisites or exclusions are given.

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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