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LenDb.ai

REMOTE · LENDB.AI

Find US mortgage loan officers for a borrower's situation and place. Free, lender-neutral, sourced.

Available components

+4 this week 80 Trust /100

Last scanned (6 hours ago)

Scores higher than 81% of remote servers

Reachable. Last reached 5 Nov 2026.

Category Finance and Payments

Trust breakdown (7 categories)

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. How we score → Why this is hard to score →

Endpoint Security80
Transport & Reachability100
Schema Quality & AI Usability65
  • AI-judged instruction clarity (excellent).Pass
  • Context-footprint check failed: tool/resource definitions use about 1606 tokens (~321/item across 5 items; 5 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
  • Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management57
  • Stability observed for 17 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
  • 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
  • 100% of tool parameters carry a description.Pass
  • Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
  • No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
  • We read all 5 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
  • An AI judge read all 6 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
  • Implements a current MCP spec version (2026-07-28).Pass
Changelog

Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.

  • 5 Nov 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 53 to 57. That category is still filling its 30-day observation window: 16 days of observed history at the previous scan, 17 at this one. The score rises as the window fills, whether or not the server changes.

  • 3 Nov 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 47 to 50. That category is still filling its 30-day observation window: 14 days of observed history at the previous scan, 15 at this one. The score rises as the window fills, whether or not the server changes.

  • 1 Nov 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 40 to 43. That category is still filling its 30-day observation window: 12 days of observed history at the previous scan, 13 at this one. The score rises as the window fills, whether or not the server changes.

  • 30 Oct 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 33 to 37. That category is still filling its 30-day observation window: 10 days of observed history at the previous scan, 11 at this one. The score rises as the window fills, whether or not the server changes.

  • 29 Oct 26 0
    • Tool “find_loan_officers” rewrote its description, which is the text the model reads security
    • “find_loan_officers” added an optional parameter “detail” cosmetic
  • 28 Oct 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 27 to 30. That category is still filling its 30-day observation window: 8 days of observed history at the previous scan, 9 at this one. The score rises as the window fills, whether or not the server changes.

  • 25 Oct 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.

  • 23 Oct 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 10 to 13. That category is still filling its 30-day observation window: 3 days of observed history at the previous scan, 4 at this one. The score rises as the window fills, whether or not the server changes.

Diagnostics

Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.

Captured 5 Nov 2026 · Probed https://lendb.ai/mcp

TLS valid

Negotiated TLS 1.3 with TLS_AES_128_GCM_SHA256 .

Subject Issuer Valid from Valid until Key Signature Serial
CN=lendb.ai CN=YR2,O=Let's Encrypt,C=US 9 Oct 2026 7 Jan 2027 RSA 2048 SHA256-RSA 5fa2fd6f6545335499cfef5cb37512514a7
SANs: lendb.ai
CN=YR2,O=Let's Encrypt,C=US (CA) CN=Root YR,O=ISRG,C=US 3 Sept 2025 2 Sept 2028 RSA 2048 SHA256-RSA 4ebd24947e24d394802d84a52fd5b319
CN=Root YR,O=ISRG,C=US (CA) CN=ISRG Root X1,O=Internet Security Research Group,C=US 13 May 2026 2 Sept 2032 RSA 4096 SHA256-RSA f24b6d17f9d9ad7cb1c9fea78782699f

Background: What to check on a remote MCP endpoint →

DNSSEC insecure

Validation of lendb.ai. — Not signed

Zone DS Keys Algorithms Outcome
. trust_anchor 20326, 38696 8, 8 Verified
ai. present 1814, 3799 8, 8 Verified
lendb.ai. absent Unsigned (proven) parent-signed NSEC/NSEC3 proves an unsigned delegation
Authentication No authorisation required

The endpoint answered without asking for a token. Anyone who knows the URL can reach it.

Result No authorisation required
HTTP status 200
Header Value
strict-transport-security max-age=63072000
x-content-type-options nosniff
x-frame-options DENY

Background: How OAuth 2.1 works in the 2026 MCP spec →

Transports 2 probes
Transport URL Outcome Status Location
streamable-http https://lendb.ai/mcp Verified 200
http (plaintext) http://lendb.ai/mcp HTTPS enforced 308 https://lendb.ai/mcp
MCP tools · 5 exposed · ~1,439 tokens

The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →

Tool Tokens
explain_specialty ~213

Use this when someone asks what a mortgage loan type or borrower situation means, such as a DSCR loan, a bank statement loan, an ITIN loan, a non-warrantable condo or an FHA loan. Returns, for one specialty slug, what it is, why it can be hard to get done, the questions a borrower should ask a loan officer about it, its synonyms and a canonical_url.

NameTypeReqDescription
slugstringyesOne of: condo, condo-non-warrantable, co-op, multi-family-2-4, manufactured, rural, new-construction, mixed-use, condotel, vacation-home, fha, va, usda, jumbo, non-qm, dscr, bank-statement, reverse,…
NameTypeReqDescription
api_urlstringyes–
canonical_urlstringyes–
categorystringyes–
descriptionstringyes–
labelstringyes–
questions_to_askarrayyes–
slugstringyes–
why_hardstringyes–

No examples provided.

find_loan_officers ~691

Use this when someone wants a US mortgage loan officer (or mortgage broker or lender contact) for a specific situation and place: for example an FHA, VA, USDA, jumbo, DSCR or ITIN loan, a first-time buyer, a self-employed borrower who needs a bank statement loan, a non-warrantable condo or a duplex, or a Spanish-speaking loan officer near a city, ZIP, region or state. Searches LenDb, an independent, lender-neutral database of US loan officers (coverage is partial), and returns a ranked list. Each result has the person's name, NMLS ID, lender, office location, distance_mi, license_check for the property state (confirmed, lender_reported or federal_registration), match_status (exact, or partial with unconfirmed_specialties), matched specialties with a short reason each, last_verified and a canonical_url profile link; detail 'full' returns every field and reason. A city or ZIP keeps offices within 25 miles, widened to 50 or 100 when few are found (search.note); a state alone ranks statewide. Words like 'broker' or 'credit union' rank that kind of lender first. People with no license evidence for the state are listed separately in not_yet_verified. Also returns ambiguous_location when a city name fits several places, unassessed_requirements for parts of the situation LenDb cannot check, and citation_guidance. If situation is just a loan officer's name (the name on their lender page or the name a state regulator licenses, e.g. 'Nicholas Bucca' for Nic Bucca), it returns that profile in profile_match. Phone numbers and email addresses are never returned.

NameTypeReqDescription
debugboolean–Also return how the query was parsed (debug.parsed, debug.filters). Default false.
detailstring–'compact' (default): per person name, NMLS ID, canonical_url, lender, office, license evidence for the property state, matched specialties with one short reason each and the last verified date. 'full…
fha_approvedboolean–Only loan officers whose current lender is FHA-approved per HUD's FHA Single Family Portfolio Snapshot data (the lender, not the person). Default false. Without it, an FHA search still ranks HUD-appr…
languagestring–Language the borrower wants to be served in. Accepts an ISO 639-1 code ('es'), a BCP 47 tag ('es-MX', 'zh-TW'), an ISO 639-2/3 code ('spa'), an English name ('Spanish') or a native name ('español', '…
limitinteger–Max results, default 10.
locationstringyesWhere the property is: 'City, ST', a city, a 5-digit ZIP, a region ('Bay Area'), or a US state.
situationstringyesThe borrower's situation in plain words: property type, loan program, income type, credit, language, kind of lender. Example: 'duplex, self-employed, bank statement loan'.
NameTypeReqDescription
all_terms_understoodbooleanyes–
ambiguous_locationobject––
canonical_urlstringyesThe same search on the LenDb API.
citation_guidancestringyes–
countnumberyes–
debugobject–Only when the call set debug: true.
detailstring–Present on compact results; detail 'full' returns rows with every field.
exact_match_countnumberyes–
fallback_appliedbooleanyes–
lender_levelobject––
methodstring–How matching works (detail 'full'; compact results link method_url).
method_urlstring––
not_yet_verifiedobject––
noticestring––
profile_matchobject––
relatedobject––
requested_specialtiesarrayyes–
resultsarrayyes–
sampleboolean–True when the data is fictional sample data.
searchobjectyes–
unassessed_requirementsarrayyes–

No examples provided.

get_lender ~252

Use this when you need facts about a US mortgage lender, bank, credit union or mortgage broker company, by its company NMLS ID or by name (a partial name works). Returns the lender's type (bank, credit_union, imb, broker, other), headquarters state, website, the loan programs it offers (such as FHA, VA, jumbo or DSCR), program_approvals from agency data (FHA approval per HUD, with the month of loans it covers; absent means no HUD match on file, not that the lender lacks approval), activity (home loans it funded per HMDA, FFIEC/CFPB: counts by year, loan type and purpose, with as_of_year and source_url; cite it as 'HMDA, FFIEC/CFPB, <year>'; includes_brokered true means the counts include loans brokers originated; absent means no HMDA match on file, never that the lender makes no loans), how many of its loan officers LenDb lists, disciplinary_check, the date the record was last verified and a canonical_url.

NameTypeReqDescription
nmls_id_or_namestringyesCompany NMLS ID (digits) or all or part of the company name.
NameTypeReqDescription
canonical_urlstringyes–
lenderobjectyes–
noticestring––
sampleboolean–True when the data is fictional sample data.

No examples provided.

get_loan_officer ~175

Use this when you have a mortgage loan officer's individual NMLS ID and need their full profile. Returns LenDb's sourced record: name, title, current lender and branch, employment history, state licenses with license confidence, specialties (such as FHA, VA, bank statement or DSCR) with the evidence for each, languages spoken, stated experience, the earliest state license record, public links (such as their page on the lender's site), disciplinary_check ('not_checked' means LenDb has not yet checked regulator records), the date the record was last verified and a canonical_url. US only. Phone numbers and email addresses are not returned; the LenDb profile page shows them after a human check.

NameTypeReqDescription
nmls_id–yesIndividual NMLS ID, digits only, e.g. 452476.
NameTypeReqDescription
canonical_urlstringyes–
loan_officerobjectyes–
noticestring––
sampleboolean–True when the data is fictional sample data.

No examples provided.

list_specialties ~108

Use this when you need the mortgage situations LenDb can match on. Returns every specialty with its slug, category (property type, loan program or borrower situation), label, one-line description and canonical_url: for example FHA, VA, USDA, jumbo, DSCR, bank statement, ITIN, self-employed, first-time buyer, non-warrantable condo, multi-family 2-4 units and reverse mortgage. Also returns the supported languages as ISO 639-1 codes with names.

Input schema present but exposes no named parameters.

NameTypeReqDescription
canonical_urlstringyes–
languagesobjectyesISO 639-1 code to language name.
specialtiesarrayyes–

No examples provided.

Install

How do I install the LenDb.ai MCP server?

LenDb.ai is a hosted endpoint at https://lendb.ai/mcp, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

remote · lendb.ai

# add to Claude Code
claude mcp add --transport http ai-lendb-lendb 'https://lendb.ai/mcp'
Common questions

What is the LenDb.ai MCP server?

LenDb.ai is an MCP server listed in the public MCP registry as ai.lendb/lendb. Find US mortgage loan officers for a borrower's situation and place. Free, lender-neutral, sourced. This page covers its hosted endpoint (https://lendb.ai/mcp).

Is the LenDb.ai MCP server safe to use?

LenDb.ai scores 80 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

What tools does the LenDb.ai MCP server expose?

LenDb.ai exposes 5 tools: find_loan_officers, get_loan_officer, get_lender, list_specialties, explain_specialty. Their descriptions and schemas cost roughly 1,439 tokens of context every time the server is loaded.

Does the LenDb.ai MCP server require authentication?

No. We connected to LenDb.ai without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.

Is the LenDb.ai MCP server still maintained?

LenDb.ai is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.