World Monitor
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Beschreibung vom Entwickler (englisches Original)
Cited CRE facts and deterministic calculators for agents: rent, CAM, debt sizing, IRR, parcel IDs.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
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Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Funktioniert in Claude im Web, auf dem Desktop und auf dem Handy:
https://api.factlot.com/mcpDie .mcpb-Datei des Entwicklers herunterladen
Der Link führt zur Datei des Entwicklers selbst; wir hosten sie nicht und verpacken sie nicht neu. Öffne die Datei, dann installiert Claude Desktop sie.
Führe das in deinem Terminal aus
claude mcp add --transport http factlot https://api.factlot.com/mcpIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"factlot": {
"url": "https://api.factlot.com/mcp"
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"factlot": {
"type": "http",
"url": "https://api.factlot.com/mcp"
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
ChatGPT verbindet sich nur mit Servern im Internet:
https://api.factlot.com/mcpMenünamen können sich je nach Tarif und mit der Zeit leicht unterscheiden.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Basierend darauf, wie sich dieser Server verbindet, und auf der eigenen Doku jeder App, geprüft am 10. Nov. 2026.
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ClaudeChatGPTGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatOpen WebUIRaycast
Laufen auf deinem Computer in
ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast
Tool-Namen und -Beschreibungen genau so, wie der Server sie am 8. Nov. 2026 gemeldet hat. Geschrieben hat sie der Entwickler, nicht wir; wir zeigen sie als reinen Text.
calc_rent_scheduleBuild a base-rent schedule from commencement date, term, area, starting $/SF/yr, escalations (percent or fixed $/SF), and abated months. Lease months run from the commencement day-of-month with no calendar proration (warns when commencement is mid-month). Returns rent steps, tota
calc_net_effective_rentCompute effective rent, straight-line net effective rent, and discounted net effective rent ($/SF/yr) after free rent, TI allowance, leasing commissions, and other landlord concessions. Rent is monthly in advance from the commencement day (no calendar proration), discounted at an
calc_compare_proposalsRank 2–20 lease proposals with different terms, sizes, free rent, TI, concessions, and operating-expense structures (gross, NNN, base year, expense stop, with opex growth) on one discounted $/SF/yr basis, per rentable or usable SF (load factor): effective occupancy cost (tenant p
calc_multifamily_rent_rollAnalyze an apartment rent roll (up to 5,000 units with status occupied, notice, vacant, model, or down; market and lease rent per month; lease dates; concessions as weeks free, months free, or dollars): gross potential rent, vacancy, non-revenue units, loss to lease, concessions
calc_percentage_rentPercentage (overage) rent for a lease year: net sales after exclusions over a natural or fixed breakpoint, prorated for partial years, less amounts already paid. Deterministic: use this instead of doing the math yourself; cite formula calc.percentage_rent@1 and the returned steps
calc_cam_reconciliationReconcile a tenant's annual CAM charge: exclusions, occupancy gross-up of variable expenses, pro rata share, controllable-expense caps (compounding_from_base, simple_from_base, year_over_year), an admin fee (before or after the cap, inside or outside it, with per-line exclusions)
calc_cam_reconciliation_batchReconcile CAM for a whole building in one call: the expense pool and gross-up once, then up to 500 tenants each with share, base year or expense stop, cap, admin fee, occupancy, estimates paid, and lease-specific excluded categories. Returns each tenant's statement (identical to
calc_capex_recoveryRecoverable capital expenditure under a lease clause: amortize a capital cost over its useful life or the lease-specified period, straight-line (declining interest) or level payment, with interest on the unamortized balance or none; optionally cap each year's recovery at demonstr
calc_debt_sizingSize the maximum loan under any combination of max LTV, min DSCR, and min debt yield for a given NOI, rate, and amortization. Reports the binding constraint, debt service, and resulting metrics; optionally a stress table over NOI, rate, and value shocks (max loan, binding constra
calc_lease_remeasurement_asc842Remeasure a lessee's lease liability and right-of-use asset under ASC 842 after a modification, lease-term reassessment, contingency resolution, or residual value guarantee change: new liability (PV of remaining payments), liability and ROU adjustments with any gain (ASC 842-20-3
calc_prepayment_and_defeasancePrepayment premium, total payoff, and cost as a share of balance for a fixed-rate amortizing or interest-only loan under yield maintenance (PV of remaining payments or rate differential, with a minimum and an explicit discounting convention), a step-down schedule, a flat percenta
calc_waltWeighted average remaining lease term in years as of a date (actual days ÷ 365.25), weighted by each lease's area_sf (weight_by "area") or annual_rent (weight_by "rent"). Deterministic: use this instead of doing the math yourself; cite formula calc.walt@1 and the returned steps,
calc_npvNPV of periodic cash flows (t = 0, 1, 2, ...; the t = 0 flow is not discounted) at a per-period discount rate given as a decimal. Deterministic: use this instead of doing the math yourself; cite formula calc.npv@1 and the returned steps, and surface any warnings.
calc_irrPeriodic and annualized IRR of periodic cash flows, as decimals (0.085 = 8.5%), solved deterministically by scanning a 1-point grid of per-period rates and bisecting each sign change; reports every root it finds when cash flows have multiple IRRs (two roots within one grid step c
calc_lease_vs_ownCompare leasing with buying over a fixed horizon: NPV of each case's annual cash costs (rent, free rent, tenant expenses, and move-in costs versus equity, debt service, owner expenses, reserves, and a sale at the horizon by stated value or exit cap rate), optionally after tax, wi
calc_renew_vs_relocateCompare renewing in place with moving over one horizon: NPV and discounted effective rent of each case (rent schedule with free rent, TI net of allowance, fees, moving, downtime, double rent, termination, and disruption costs), the starting rent at which they break even, and year
calc_hotel_metricsOccupancy, ADR, RevPAR, TRevPAR, and a USALI-style profit ladder for a period: departmental profit, GOP and margin, GOPPAR, management fees, EBITDA, FF&E reserve, and NOI (EBITDA less replacement reserve). Deterministic: use this instead of doing the math yourself; cite formula c
calc_dcf_pro_formaProperty-level discounted cash flow from an in-place rent roll: each lease's rent, escalations, remaining term, and expense recovery (NNN, gross, base year), then at expiration one blended lease weighted by the renewal probability (renewal vs new-tenant rent, free rent, TI, leasi
calc_direct_capValue by direct capitalization: (NOI − capital reserve) ÷ cap rate, then below-the-line deductions for lease-up costs, free-rent burn-off, and other adjustments to an as-is value (optionally rounded); and the cap rate a price implies, before and after reserves and all-in after th
calc_amortization_schedulePeriod-by-period schedule of a fixed-rate loan, monthly or quarterly: an optional interest-only period, then level payments on an amortization term that can run past maturity (balloon), with 30/360, actual/360, or actual/365 interest (the payment on a 30/360 basis). Rows are in c
calc_equity_waterfallSplit periodic project equity cash flows between LP and GP: preferred return (compounding or simple) and return of capital pro rata to capital in either order, an optional GP catch-up, IRR-hurdle tiers with promote splits measured on the LP's IRR, and a residual split. Returns pe
calc_exchange_1031Not tax advice: arithmetic only. The 45-day identification and 180-day exchange deadlines of IRC §1031(a)(3) (the exchange period ends earlier at a given tax-return due date), the three-property, 200%, and 95% identification tests of Treas. Reg. §1.1031(k)-1(c)(4), and the cash b
calc_critical_datesDated list of a lease's notice deadlines and option windows, from its expiration date and options: each renewal option's notice deadline or notice window and renewal term (successive options count back from the term before), each termination option's last day to give notice, an e
calc_reconcile_dcfReconcile FactLot's DCF with another system's export (ARGUS or any other model; parity is not claimed and is untested against real ARGUS output): give the calc_dcf_pro_forma input plus the other system's annual lines {label, values: [year 1, …]}, optionally its value, unlevered I
calc_reconcile_camReconcile FactLot's CAM reconciliation for one expense pool with another system's per-tenant results (Yardi or any other; parity is not claimed and is untested against a real export): give the calc_cam_reconciliation_batch input plus, per tenant, what the other system reported (s
calc_tx_unequal_appraisalEvidence calculation only: not an appraisal or an opinion of value, not legal or tax advice, and not a substitute for the advice of an attorney. The arithmetic of Tex. Tax Code §41.43(b)(3) and §42.26(a)(3), the subject's value against the median appraised value of a reasonable n
rules_tx_property_tax_protest_deadlineDeadline to file a Texas property tax protest with the appraisal review board: the later of May 15 or 30 days after the notice of appraised value was delivered, rolled past weekends and Texas legal holidays, plus a conservative file-by date. Returns the statute citations and the
id_parcelDeterministic FactLot ID for a parcel from state FIPS, county FIPS, and the assessor's parcel number (APN). The same parcel always yields the same ID, offline. Use it as the join key across systems. APNs that differ only in where a separator falls ("12-34", "1-234") get different
id_entityDeterministic FactLot ID for a legal entity (LLC, LP, corporation, trust) from its registry jurisdiction and file number, or from its LEI (an LEI whose ISO 17442 check digits do not match is rejected).
id_spaceFactLot ID for a suite or unit within a building, given the building's FactLot URN (from id_building). Suite designators are normalized: "Suite 100", "Ste 100" and "#100" agree, while "1-20" and "120" stay distinct.
id_buildingDeterministic FactLot ID for a building from its street address: county FIPS, ZIP code, and street line. Every condominium unit, suite, and floor at the address gets the same building ID, offline. Pass it to id_space for a suite, or to registry_get to see the building's parcels.
id_documentContent-addressed FactLot ID (SHA-256) for a document such as a lease, amendment, or estoppel.
id_parseValidate a FactLot URN and return its kind, components, and short ID.
fact_validateCheck one FactLot fact against the published fact schema (spec/schemas/fact.json): subject URN, dotted predicate, as_of date, provenance, optional rights; unknown fields are rejected at every level. Rules: every fact except a computed one cites at least one source (an inferred fa
doc_verify_factsCheck values YOU extracted from a document against its text, deterministically; FactLot runs no model. Send the pages as text and one candidate per value: {predicate, value, quote, page}. Each candidate is checked in order: (1) the value's shape (Ingest predicates such as lease.e
doc_abstract_leaseCurrent-state lease abstract from a lease and its amendments, with amendment precedence, as of as_of. Give each document's pages with the candidates you extracted (checked exactly as doc_verify_facts checks them), or the facts doc_verify_facts already returned for it (without pag
doc_compareCompare values another document asserts (an estoppel, rent roll or offering memo, mapped by you to predicates) with verified lease facts from doc_abstract_lease or doc_verify_facts. Each asserted value comes back match, mismatch (with the differing fields), not_in_lease, or ambig
doc_verify_claimCheck one claim against a document page: {predicate, value} (anything doc_verify_facts accepts) or a short claim sentence ({text} or a bare string), plus the verbatim quote and page it rests on. Returns supported (the quote is on the page and every checked number, date and name i
doc_spread_statementSpread a T-12, operating statement, budget or rent roll summary YOU read, onto FactLot's published chart of accounts (multifamily, commercial, or hotel after the USALI summary), deterministically; FactLot runs no model. Send the pages as text and one line per item: {label, amount
doc_check_use_conflictCheck a proposed use (a description, plus an optional use category or 2022 NAICS code) against a center's verified lease.exclusive_use and lease.prohibited_uses facts (from doc_verify_facts or doc_abstract_lease; each must pass fact_validate). Deterministic and textual: a restric
doc_predicatesLook up the lease predicates doc_verify_facts and doc_abstract_lease know (lease.expiration_date, lease.base_rent_schedule, lease.go_dark, ...) before extracting: for each, its value shape as JSON Schema, one valid example with a quote that states it (and whether that example ver
fact_package_createBundle FactLot facts (each must pass fact_validate) into a fact package a counterparty can verify: canonical JSON (RFC 8785) of every fact, an RFC 6962 Merkle tree over the facts, the source documents the facts cite with their sha256 where known, and a digest over all of it. Retu
fact_package_verifyVerify a fact package from fact_package_create: recompute every fact's hash, the Merkle root and the digest from the content (any changed fact, title, date or document list shows as a mismatch); check each fact against fact_validate; check the Ed25519 signature, against the publi
lease_comp_validateCheck a lease comparable one firm shares with others, as a FactLot fact with predicate lease.comp, and derive its net effective rent. The fact goes through fact_validate (schema, citations, rights), then the comp's own rules: shape (spec/schemas/fact.json LeaseComp: area, commenc
lease_comp_searchSearch the 6,994 lease comps from the U.S. General Services Administration's Lease Inventory (October 2026 file, FactLot release lease-comps-gsa-2026-11-04), bundled with FactLot: works offline and on the hosted API, no registry needed. READ THE CAVEAT: the tenant in every one of
registry_getEverything the FactLot Registry knows about a parcel, legal entity, or building: status, current cited facts (owner of record, situs address, property category, areas, values), ownership links, external IDs, and for a parcel the building it belongs to (condominium units roll up i
registry_resolve_parcelFind a parcel's FactLot URN from state FIPS, county FIPS, and APN, if the registry holds it. A miss returns urn null with coverage: whether that county is loaded in this registry. Requires a FactLot Registry (public-record facts about commercial property, each cited to the source
registry_resolve_externalFind the FactLot URN for an ID from another system, e.g. system "tx-harris-cad-account" or "us-tx-taxpayer". Requires a FactLot Registry (public-record facts about commercial property, each cited to the source file and record).
registry_search_addressFind commercial parcels by situs address. Matches whole words of the street line (the part before the first comma), normalized: 'Street' = 'St', 'Northwest' = 'N.W.' = 'NW'; a suite, unit, '#' or floor is ignored. Give a house number ('1200 Main', '1200 Main St NW') or a street n
registry_search_entitiesFind registered business entities (LLCs, LPs, corporations) by name. Natural persons are not searchable. Requires a FactLot Registry (public-record facts about commercial property, each cited to the source file and record).
registry_owned_byCommercial parcels currently linked to a legal entity as owner of record (inferred links carry a confidence), each with the building it belongs to, plus a per-building summary so condominium units count once per building. Requires a FactLot Registry (public-record facts about com
registry_coverageThe counties (with parcel and building counts), entity registries, and crosswalk systems this FactLot Registry file holds, each with its sources and as-of dates. Call it before concluding that a parcel or entity does not exist: a miss outside these regions means not loaded, not a
registry_tx_equity_candidatesCandidate comparable parcels for a Texas unequal-appraisal comparison (Tax Code §41.43(b)(3)), from one tax year of the subject's appraisal district roll (the newest year whose roll gives the subject a value, or tax_year), with every field of the subject and the candidates read f
searchSearch FactLot's knowledge base (tools, inputs, CRE predicates, ID and fact specifications). Returns matching document ids, titles, and URLs; call fetch for full text. A query on a subject the knowledge base does not cover returns no results and a note.
fetchFetch the full text of a FactLot knowledge document by the id returned from search. With a registry loaded, a registry fact's source document_id (fl:document:sha256-…) returns that source snapshot: publisher, dataset, URL, retrieval date, as-of date, licence, AI-use and redistrib
schema_getReturn the FactLot data model (Fact, Provenance, SourceRef, Rights, Relationship) as JSON Schema.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 10. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto 9mp96ygnbk-alt bestätigt, dem das Repo gehört
Code zuletzt geändert: gestern
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Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Beides geht: Installiere ihn auf deinem Computer oder verbinde dich mit der Cloud-Version.
Das GitHub-Repo wurde zuletzt gestern geändert, und die neueste Version (0.5.0) kam vor 3 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Er verbindet sich per URL, also funktionieren auch Apps, die Remote-MCP-Server akzeptieren, zum Beispiel ChatGPT im Entwicklermodus.
In derselben Kategorie: World Monitor, Claude Flow, Tldraw
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:↓ Downloads pro Wochei★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
Real-time global intelligence on conflict, markets, energy, trade, shipping and cyber. 102 tools.
AI orchestration with hive-mind swarms, neural networks, and 87 MCP tools for enterprise dev.
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The open fact layer for commercial real estate AI agents. Deterministic CRE math with an audit trail, canonical property IDs, and public-record facts that cite the document they came from. Free to use hosted, free to run yourself, open source.
No key, no install:
curl -s https://api.factlot.com/v1/calc/debt_sizing -H 'content-type: application/json' \
-d '{"noi":1000000,"value":15000000,"rate_annual":0.065,"amortization_years":30,"max_ltv":0.65,"min_dscr":1.25}'
The answer is a $9,750,000 maximum loan, bound by loan-to-value (the coverage test alone would allow $10.5 million), with every intermediate step, its formula, and the versioned formula ID calc.debt_sizing@3 so an agent can cite exactly how the number was made.
Check what your agent read from a lease the same way: it extracts each term with the exact quote and page, and doc_verify_facts confirms the quote is on that page and states the value, then returns cited facts or the reason it could not (document verification). No model runs on FactLot's side and nothing is stored.
Check us against your own spreadsheet: seven Excel workbooks (office, industrial, retail, hotel, lender, lease accounting and more) put live Excel formulas beside FactLot's numbers with a difference column of 0.00 (synthetic inputs; ARGUS parity is untested and not claimed). What the registry holds, county by county, and the licence and AI-use rights of every source: coverage and rights.
Lease comps, free: 6,994 federal (GSA) lease comps (5.3 MB, public domain) as cited public-record facts, and an open format with lease_comp_validate so firms can share comps with each other, each with stated rights, no names, and a derived net effective rent (lease comps). GSA leases are government-tenant leases, not market comps for every use. Search them from any agent, hosted, with lease_comp_search.
| Client | How |
|---|---|
| Claude Code plugin | /plugin marketplace add 9mp96ygnbk-alt/factlot, then /plugin install factlot@factlot: twelve CRE skills (loan sizing, lease economics, CAM, ASC 842, returns and more) wired to the hosted tools (plugin) |
| Claude Code | claude mcp add --transport http factlot https://api.factlot.com/mcp |
| Claude (web and Desktop) | Settings, Connectors, Add custom connector: https://api.factlot.com/mcp. Or open factlot-0.5.0.mcpb |
| ChatGPT | Developer mode app with https://api.factlot.com/mcp, or a Custom GPT Action importing https://api.factlot.com/openapi-3.0.json |
| Cursor, VS Code, other clients | Remote MCP URL https://api.factlot.com/mcp (guides) |
| Any MCP client, run locally | npx -y https://github.com/9mp96ygnbk-alt/factlot/releases/download/v0.5.0/factlot-mcp-standalone-0.5.0.tgz (Node.js 22.6+, no network calls) |
| Python agents | pip install "git+https://github.com/9mp96ygnbk-alt/factlot.git@v0.5.0#subdirectory=python" |
| Self-hosted REST and MCP | docker run -p 8787:8787 ghcr.io/9mp96ygnbk-alt/factlot:0.5.0 |
The hosted endpoint is free with no key at 60 requests a minute. It is listed in the official MCP Registry as io.github.9mp96ygnbk-alt/factlot. Runnable examples for REST, Python and agent SDKs are in examples/.
Try the registry tools on real data: the DC registry sample (23.5 MB, CC BY 4.0) holds 18,077 District of Columbia commercial parcels, 5,513 owning entities, 7,931 cited ownership links, 396 buildings, zoning districts for 16,648 parcels and the latest recorded sale for 12,594.
curl -fLO https://github.com/9mp96ygnbk-alt/factlot/releases/download/registry-sample-dc-2026-11-07/factlot-registry-sample-dc-2026-11-07.db.gz && gunzip factlot-registry-sample-dc-2026-11-07.db.gz
FACTLOT_REGISTRY_DB="$PWD/factlot-registry-sample-dc-2026-11-07.db" npx -y https://github.com/9mp96ygnbk-alt/factlot/releases/download/v0.5.0/factlot-mcp-standalone-0.5.0.tgz
Plans: the hosted endpoint is free with no key: every calculator, ID, fact, rules and document tool at 60 requests a minute per IP address, 25 registry lookups a day per IP address, and 3 reports a month per IP address. Pro, $49 a month: 1 API key, 600 requests a minute, 5,000 registry lookups a month, 50 reports a month, email support. Team, $599 a month: up to 5 API keys, 3,000 requests a minute, 100,000 registry lookups a month, 1,000 reports a month, priority support, and a vote on which regions open next. Limits are hard caps with no overage charges; at a cap the call returns HTTP 429 with the reset time. Paid plans renew monthly until cancelled; cancel any time. A registry download licence ($2,400 a year per state file, refreshed weekly, for internal use, not redistribution) is available on request, and Enterprise (private deployment, invoicing, a signed data-processing agreement with a no-training commitment, and SSO) is by quote: contact@factlot.com. Terms · Privacy. Questions and bug reports go in GitHub Discussions and Issues.
The hosted API at https://api.factlot.com answers registry lookups on every plan, including the free tier with no key (25 registry lookups a day per IP address). These regions are hosted:
| Region | Hosted data | Licence |
|---|---|---|
| Maryland | All 24 jurisdictions: 109,249 commercial parcels and 2,812 buildings, SDAT data as of 2026-11-05 | Public domain |
| District of Columbia | 18,077 commercial parcels, 5,513 registered business entities and 396 buildings, with owners of record (organisations only), zoning and recorded sales | CC BY 4.0 |
| Utah | Juab and Summit counties: 1,528 parcels, snapshot as of 2025-11-26 | CC BY 4.0, "State of Utah, SGID" |
| Texas | Harris (87,717 parcels and 3,511 buildings), Dallas (59,645), Travis (30,556), Collin (18,035), Denton (13,304) and Williamson (11,758) counties: 221,015 commercial parcels with value history by tax year, and the 68,804 Comptroller entities that own them. Denton and Williamson are 2027 preliminary rolls as of 2026-11-07. Tarrant County is not served | Public record, cited per fact; organisation owners only |
Maryland, DC, Utah and Texas hold 349,869 parcels in 33 counties. Call registry_coverage first: it lists exactly what is hosted, and a lookup outside it returns a coverage miss. Every answer cites its source file and the licence or attribution that goes with it. The registry is built to keep owners that are organisations and to exclude natural persons' names (if a record names you as a person, or is wrong, email contact@factlot.com and we will remove or correct it), and the Maryland and Utah files carry no owner names. The DC data is the same as the downloadable sample registry-sample-dc-2026-11-07.
The other regions in the project's pipeline (New York City and more) are available only on a self-hosted FactLot with a registry file; more hosted regions open as their terms allow. Tarrant County is not served by the hosted registry while the publisher's terms for AI use are unclear.
Agents doing CRE work get stuck in the same places. They can't trust their own arithmetic, they can't cite where a number came from, and they can't tell that "Suite 400, 123 Main LLC" in one system is the same thing as a parcel number