Skip to main content
Glama
sugukurukabe

japan-real-estate-intel

レバレッジ10年キャッシュフロー試算

simulate_leveraged_cashflow
Read-only

Analyze leveraged real estate investments by inputting loan terms, rent, costs, and exit assumptions to generate annual cash flows, DSCR, IRR, and sensitivity.

Instructions

Leveraged 10-year real estate pro-forma: accepts loan interest rate, LTV/loan amount, rent, vacancy, operating costs, property tax, depreciation and exit assumptions, then returns annual NOI, debt service, after-tax cash flow, DSCR, IRR, equity multiple and sensitivity. | 銀行借入の利率・LTV・賃料・空室率・経費・固定資産税・減価償却・出口条件から10年の年次収支、税引後CF、DSCR、IRR、感応度を試算する。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
prefectureNo都道府県名(和名/英名/ISO 3166-2 コード対応)愛知県
cityYes市区町村(例: '名古屋市中区', '新宿区')
districtNo町丁目・地区名(任意)
propertyTypeNo物件種別mansion
askingPriceYes購入価格・売出価格(円)
purchaseCostNo仲介手数料・登記費用など初期取得費用(円)
renovationCostNo初期修繕・リノベーション費用(円)
landValueRatioNo土地按分比率。建物減価償却のために使用(0-1)
annualRentYes初年度の想定年間賃料収入(円)
otherIncomeAnnualNo駐車場・看板等のその他年間収入(円)
vacancyRateNo初年度の想定空室率(0-1)
operatingExpenseAnnualNo管理費・修繕費・保険料など年間運営費(円)
propertyTaxAnnualNo固定資産税・都市計画税等(円/年)
annualCapexNo毎年の資本的支出・大規模修繕積立相当(円/年)
loanYes銀行借入条件
assumptionsNo10年収支・税務前提
output_modeNoOutput verbosity. compact=TL;DR + key numbers only (default), detailed=full Markdown report | 出力詳細度。compact=主要数値のみ(デフォルト)、detailed=全文レポート付きcompact

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
prefectureYes
cityYes
districtYes
summaryYes
summaryKpisYes
assumptionsYes
yearlyRowsYes
sensitivityYes
redFlagsYes
recommendationsYes
markdownReportYes
dashboardUriYes
attributionYes
Behavior4/5

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

Annotations already indicate readOnlyHint=true (safe simulation) and destructiveHint=false. The description adds context by listing specific outputs (NOI, IRR, sensitivity) and confirming the tool returns annual projections, which goes beyond annotations. No contradiction with annotations.

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, information-dense sentence covering inputs and outputs, with a Japanese translation appended. It is front-loaded and efficient, though slightly verbose due to the bilingual addition. Every clause earns its place.

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 the high parameter count (17) and the existence of an output schema (per context), the description provides a complete overview of the tool's purpose and behavior. It mentions return values (NOI, DSCR, IRR, etc.) and sensitivity analysis, leaving no major gaps for an agent to understand what to expect.

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?

The input schema has 100% description coverage, so parameter details are already documented. The description summarizes broad categories (loan, rent, costs) but does not add new semantic meaning beyond what the schema provides. Baseline 3 is appropriate.

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 it is a leveraged 10-year real estate pro-forma, listing key inputs (loan interest rate, LTV, rent, etc.) and outputs (NOI, debt service, DSCR, IRR, etc.). It distinguishes itself from sibling tools like 'scenario_what_if' by focusing specifically on leveraged cashflow with debt service and returns. The bilingual description reinforces purpose.

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?

The description implies use for simulating leveraged real estate investments but does not explicitly state when to use this tool versus alternatives (e.g., 'scenario_what_if' or 'analyze_renovation_yield'). No exclusion criteria or prerequisites are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/sugukurukabe/japan-real-estate-intel-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server