io.github.Zwondra/cre-intelligence-mcp

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Au dernier audit MCP Hub, le serveur MCP « io.github.Zwondra/cre-intelligence-mcp » est classé « Non encore testé ». Référencé dans l'annuaire. Seul le pentest comportemental fait foi, et il n'a pas encore été exécuté sur ce serveur. Le verdict est réévalué en continu ; consultez la fiche pour l'état à jour.

Méthode : MCP Hub analyse la description de chaque outil (couche 1) et, pour le verdict fort, exécute le serveur dans une micro-VM isolée avec leurres et réseau bloqué (couche 2). On publie des noms et des compteurs, jamais le contenu brut renvoyé par un outil.

Les 13 outils

• abstract_lease≈ 129 tokens

  Extract all key terms from a commercial lease document. Returns term, base rent schedule, escalations, TI allowance, CAM structure, renewal options, termination rights, exclusivity, co-tenancy, and red flags.

  Paramètres, retour & exemple (1)

  Paramètres

  • text · string · requis
    Raw text copied from a commercial lease PDF

  Exemple d'appel

  {"name": "abstract_lease", "arguments": {"text": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• analyze_rent_roll≈ 168 tokens

  Extract structured tenant and lease data from a rent roll document. Paste the text content of your rent roll PDF here (copy-paste from PDF reader). Returns tenant list, suite/SF, lease dates, monthly rent, escalations, and options.

  Paramètres, retour & exemple (2)

  Paramètres

  • text · string · requis
    Raw text copied from a rent roll PDF
  • property_name · ? · optionnel
    Optional property name for context

  Exemple d'appel

  {"name": "analyze_rent_roll", "arguments": {"text": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• build_dcf_model≈ 350 tokens

  Build a levered DCF model using live Federal Reserve rates. Automatically fetches current SOFR to derive the loan rate if not provided. Returns: annual cash flows, IRR, equity multiple, cash-on-cash, DSCR, and exit analysis.

  Paramètres, retour & exemple (8)

  Paramètres

  • loan_rate · ? · optionnel
    Loan interest rate % — if None, fetches live SOFR + 175bps
  • noi_year1 · number · requis
    Year 1 Net Operating Income ($)
  • equity_pct · number · optionnel
    Equity as % of purchase price (default 35%)
  • hold_years · integer · optionnel
    Hold period in years (default 10)
  • exit_cap_rate · ? · optionnel
    Exit cap rate % — if None, uses entry cap + 25bps (conservative)
  • purchase_price · number · requis
    Acquisition price ($)
  • noi_growth_rate · number · optionnel
    Annual NOI growth rate % (default 3.0)
  • amortization_years · integer · optionnel
    Loan amortization period (default 30 years)

  Exemple d'appel

  {"name": "build_dcf_model", "arguments": {"noi_year1": 1, "purchase_price": 1}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• export_dcf_excel≈ 416 tokens

  Generate a downloadable Excel (.xlsx) underwriting model with LIVE formulas — editable assumptions, PMT/FV amortization, IRR, equity multiple, a sensitivity grid, live Fed rates, and (if an address is given) Census trade-area demographics. Returns a download link valid for 60 minutes.

  Paramètres, retour & exemple (10)

  Paramètres

  • address · ? · optionnel
    Optional property address — adds a demographics sheet
  • loan_rate · ? · optionnel
    Loan rate % — default live SOFR + 175bps
  • noi_year1 · number · requis
    Year 1 Net Operating Income ($)
  • equity_pct · number · optionnel
    Equity as % of price (default 35)
  • hold_years · integer · optionnel
    Hold period (default 10)
  • exit_cap_rate · ? · optionnel
    Exit cap % — default entry cap + 25bps
  • property_name · ? · optionnel
    Optional label for the model header
  • purchase_price · number · requis
    Acquisition price ($)
  • noi_growth_rate · number · optionnel
    Annual NOI growth % (default 3.0)
  • amortization_years · integer · optionnel
    Amortization (default 30)

  Exemple d'appel

  {"name": "export_dcf_excel", "arguments": {"noi_year1": 1, "purchase_price": 1}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• flag_lease_risks≈ 135 tokens

  Analyze a parsed rent roll for investment risks. Feed the output from analyze_rent_roll directly into this tool. Returns: rollover risk, tenant concentration, credit risk, and actionable recommendations.

  Paramètres, retour & exemple (1)

  Paramètres

  • rent_roll_json · string · requis
    JSON string from the analyze_rent_roll tool output

  Exemple d'appel

  {"name": "flag_lease_risks", "arguments": {"rent_roll_json": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• generate_deal_memo≈ 300 tokens

  Generate a formatted CRE acquisition memo / Investment Committee memo. Automatically pulls live rates from FRED and demographics from Census Bureau to provide real market context — not guesses.

  Paramètres, retour & exemple (6)

  Paramètres

  • noi · number · requis
    Net Operating Income ($)
  • asking_price · number · requis
    Asking price ($)
  • property_type · string · requis
    Multifamily / Office / Retail / Industrial / Mixed-Use
  • property_address · string · requis
    Full property address
  • rent_roll_summary · ? · optionnel
    Optional: paste output from analyze_rent_roll or flag_lease_risks
  • additional_context · ? · optionnel
    Any additional deal notes, seller info, market color

  Exemple d'appel

  {"name": "generate_deal_memo", "arguments": {"noi": 1, "asking_price": 1, "property_type": "texte", "property_address": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}], "structuredContent": {"result": "texte"}}

• get_cre_market_data≈ 112 tokens

  Get Commercial Real Estate price index and broader market data from the Federal Reserve. Returns CRE price trends, office/retail/industrial vacancy proxies, and credit spreads. Provides macro context for deal underwriting and cap rate analysis.

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "get_cre_market_data", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• get_current_rates≈ 134 tokens

  Get live interest rates from the Federal Reserve (FRED). Returns SOFR, 10-year Treasury, 5-year Treasury, Fed Funds Rate, and 30-day SOFR average. Also calculates implied cap rate ranges based on current treasury spreads. Use this BEFORE any DCF model or loan underwriting. These are real-time numbers Claude cannot access on its own.

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "get_current_rates", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• get_inflation_data≈ 108 tokens

  Get current CPI and rent inflation data from the Federal Reserve. Returns overall inflation, shelter inflation, and rent-specific CPI with YoY changes. Use this to calibrate rent growth assumptions in your DCF model — don't guess.

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "get_inflation_data", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• get_market_demographics≈ 182 tokens

  Get Census Bureau demographics for any US property address. Returns median income, population, employment rate, housing vacancy, median rents, and education levels for the census tract. This is address-specific data from the actual Census tract — not estimates. Claude cannot access this without the MCP. For 1/3/5-mile trade-area rings, use get_radius_demographics instead.

  Paramètres, retour & exemple (1)

  Paramètres

  • address · string · requis
    Full US property address (e.g. "1234 Main St, Charlotte, NC 28202")

  Exemple d'appel

  {"name": "get_market_demographics", "arguments": {"address": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• get_radius_demographics≈ 245 tokens

  Get aggregated Census demographics for radius rings around a US property address — the standard 1/3/5-mile trade-area format used in CRE site analysis. Aggregates every census tract whose centroid falls within each radius: population, household-weighted median income, employment rate, college attainment, housing vacancy, renter share, and median rent. Use this for trade-area / site analysis. Use get_market_demographics for the single census tract immediately around the address.

  Paramètres, retour & exemple (2)

  Paramètres

  • address · string · requis
    Full US property address (e.g. "1234 Main St, Charlotte, NC 28202")
  • radii_miles · string · optionnel
    Comma-separated radii in miles (default "1,3,5", each capped at 15)

  Exemple d'appel

  {"name": "get_radius_demographics", "arguments": {"address": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• screen_land_market≈ 249 tokens

  Screen a US county as a LAND-INVESTING market (raw-land flip / Podolsky style). Grades the county on the signals that matter for buying cheap rural land and reselling on terms: population growth, demographics, owner share, and affordability. IMPORTANT: This screens on FREE Census data only (growth + demographics + a home-value affordability proxy). It does NOT include actual land sale prices or comps — those require county records or a paid service, and must be verified per-parcel before buying. Use this to rank/shortlist markets, not to buy.

  Paramètres, retour & exemple (2)

  Paramètres

  • state · string · requis
    2-letter state abbreviation (e.g. "AZ") or 2-digit state FIPS
  • county · string · requis
    County name (e.g. "Mohave" or "Mohave County")

  Exemple d'appel

  {"name": "screen_land_market", "arguments": {"state": "texte", "county": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• screen_parcel_dd≈ 245 tokens

  Pre-screen a land parcel's location for the AUTOMATABLE due-diligence red flags: FEMA flood zone and federal wetlands. Pulls live from FEMA's National Flood Hazard Layer and the US Fish & Wildlife National Wetlands Inventory. Use this to kill obviously-bad parcels (flood zone, wetlands) at scale BEFORE spending time on manual due diligence. IMPORTANT: Checks flood + wetlands only. It does NOT check legal ACCESS (landlocked — the #1 land deal-killer), title/liens, or zoning — those stay MANUAL, per-parcel checks via county records. A clean screen here is necessary, NOT sufficient.

  Paramètres, retour & exemple (2)

  Paramètres

  • lat · number · requis
    Parcel latitude (decimal degrees)
  • lng · number · requis
    Parcel longitude (decimal degrees)

  Exemple d'appel

  {"name": "screen_parcel_dd", "arguments": {"lat": 1, "lng": 1}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

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Source

https://cre-intelligence-mcp.onrender.com/mcp