mtgmon Mortgage Analytics

Read-only U.S.

CommunityDegradedNo sign-inGlobalFreeRead-only

What it can do

    What data it sees

    Do you need an account

    No: the server works without sign-in

    Read-only U.S. mortgage market, lender, GSE performance, and servicing analytics.

    How to connect

    How to connect mtgmon Mortgage Analytics to Claude

    The server address to paste into Claude settings:

    https://mtgmon.com/mcp

    1. Open Claude (claude.ai or the desktop app).
    2. Go to Settings → Connectors.
    3. Click Add custom connector.
    4. Paste the server address copied below into Remote MCP server URL and click Add.
    5. In a chat, click + → Connectors and switch the new connector on.

    Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

    Authorization

    After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

    How to connect mtgmon Mortgage Analytics to ChatGPT

    The server address to paste into ChatGPT settings:

    https://mtgmon.com/mcp

    1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
    2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
    3. Open Settings → Connectors and click Create.
    4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
    5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
    6. In a new chat open + → Apps/Connectors and enable the connector.

    OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

    Authorization

    On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

    How to connect mtgmon Mortgage Analytics to Cursor

    The server address to paste into Cursor settings:

    https://mtgmon.com/mcp

    Fastest: click Open in Cursor below and confirm the prompt.

    Manually:

    1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
    2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
    3. Save the file. The server appears in the MCP list; authorize it there if asked.

    Authorization

    If the service needs sign-in, an authorize button appears next to the server in the MCP list.

    Server tool list (10)

    Raw names from tools/list. Only developers need these.

    get_market_snapshotLatest U.S. mortgage rates, TBA par coupons, Treasury and SOFR benchmarks, and mtgmon's fitted MBS basis in one call. No arguments. Use for: "what are mortgage rates today", 30y vs 15y, current coupon, 2y/10y Treasury, SOFR swap levels, whether MBS spreads are rich or cheap versus the model. Do not use for: rate history (not exposed here) or GSE loan performance (get_loan_performance). Returns data.rates, a list of up to 9 points {series, date, value, previous_value, change} for series MORTGAGE30US and MORTGAGE15US (Freddie Mac PMMS, weekly), tba_par_coupon_30y and tba_par_coupon_15y, 2Y and 10Y Treasury, and SOFR O/N, SOFR 2Y, SOFR 10Y; plus data.mbs_basis {model_version, latest_date, latest_basis_bps, latest_fair_bps, latest_gap_bps, latest_gap_zscore, r2_level_full_sample, ec_half_life_days, ...}. Rates are percent, basis is basis points. Series have different observation dates; quote each point's own date. Behavior: read-only, no auth or API key, no PII, idempotent and safe to retry. The public endpoint https://mtgmon.com/mcp allows about 60 requests/min and 250 tool calls/day per client IP. Result shape is {data, meta}; meta carries available, as_of, source_scope, weighting, units, cache_status, caveats, and truncated. If meta.available is false the data is unavailable or the cache is still warming (retry after a few seconds); never report it as zero.
    get_loan_performanceFannie Mae and Freddie Mac single-family loan-performance analytics for exactly one topic. Topics: - credit: monthly origination FICO and LTV (origination-UPB weighted). data.series {fico, ltv} and data.latest. - prepayment: monthly CPR and SMM (UPB weighted). - delinquency: monthly 30/60/90+ day delinquency rates (UPB-weighted stock rates). - loss: monthly loss severity (percent) and net loss (USD billions), plus data.latest_waterfall. - vintage: cumulative default-rate curves by vintage year over loan age (data.series = [{vintage, points}]). - transitions: loan-level monthly flows between Current/DQ30/DQ60/DQ90+/Modified/Liquidated (data.flows, top_n rows of {from, to, loans, upb, prob}) and data.history {cure_history, roll_30_60_history, roll_60_90_history}. - modification: outcomes at 6/12/18/24/36 months after a first 90+ DPD event, Modified vs NotModified arms (data.horizons, data.outcome_states, data.cohort_size, data.pooled, data.recent_vintages). - rate_sensitivity: modelled CPR under rate shocks (data.scenario = [{rate_shock_bps, predicted_cpr}], data.model {current_rate, current_cpr, slope}). - lock_in: active book by WAC vs today's mortgage rate: data.summary {total_upb, loan_count, wac_weighted, pct_below_mtg, pct_200bps_otm, pct_100bps_otm, pct_in_money} and data.largest_wac_age_cells {wac, age_band, upb}. Use for: agency credit quality, prepayment speeds, delinquency, roll and cure rates, modification outcomes, losses, the lock-in effect. Do not use for: originator volume or market share (search_lenders / get_lender_profile), today's rates (get_market_snapshot), per-servicer performance (get_servicer_analytics). Time-series topics return data.series {metric: [{date, value}, ...]} downsampled to max_points, plus data.latest. Scope is public GSE loan-level disclosures, not the whole U.S. market; meta.as_of is the latest reporting month. Behavior: read-only, no auth or API key, no PII, idempotent and safe to retry. The public endpoint https://mtgmon.
    search_lendersFind HMDA lenders and resolve names to canonical lender keys. Call this before any other lender tool. Use for: turning a company name ('Rocket', 'UWM', 'Wells Fargo', 'PennyMac') into its canonical key, ranking lenders by origination volume, or listing lenders by charter type or headquarters state. Then pass a result's exact `name` (e.g. 'Rocket/Quicken') as `lender` to get_lender_profile, get_hmda_product_analytics, or get_servicer_analytics mode='transfers', or as lender_a / lender_b to compare_lenders. Legal names such as 'Rocket Mortgage, LLC' are not keys and return meta.available=false downstream. Returns data.results (at most `limit` rows of {name, leis, charter_type, hq_state, recent_year, recent_volume, total_loans, volume_rank, subsidiary_count}), data.matched_count, and data.total_count. recent_volume is USD originations in recent_year (the latest HMDA year). Keys are holding-company or originator groups, so subsidiaries and LEIs roll up into one key. Behavior: read-only, no auth or API key, no PII, idempotent and safe to retry. The public endpoint https://mtgmon.com/mcp allows about 60 requests/min and 250 tool calls/day per client IP. Result shape is {data, meta}; meta carries available, as_of, source_scope, weighting, units, cache_status, caveats, and truncated. If meta.available is false the data is unavailable or the cache is still warming (retry after a few seconds); never report it as zero.
    get_hmda_product_analyticsHMDA origination counts, reported dollar amounts, averages, and year-over-year growth for one product family, optionally for one property state or one lender, with an annual history back to 2018. Use for: HELOC, reverse-mortgage, or closed-end mortgage origination volume and growth; product mix by loan program (conventional/FHA/VA/USDA), purpose, lien, occupancy, construction method, or business purpose; state-level or lender-level product questions. Do not use for: lender rankings or market share (search_lenders / get_lender_profile) or GSE loan performance (get_loan_performance). Important: product_family defaults to 'heloc'. Use 'closed_end_mortgage' for ordinary mortgages or 'all' for every HMDA origination. Source is the frozen annual FFIEC/CFPB HMDA Snapshot LAR (one release per year, 2018 onward); state filters use the property state, not the lender's headquarters. Returns data.selected_year, data.current and data.previous (each {year, origination_count, reported_amount_count, reported_amount_sum, average_reported_amount, reported_amount_max, origination_count_yoy_pct, reported_amount_sum_yoy_pct, average_reported_amount_yoy_pct}), data.history (one such row per year), data.filters (applied filters), data.definition, and data.source_releases. Amounts are USD; YoY fields are percent; for HELOCs the amount is credit available, not the drawn balance. Behavior: read-only, no auth or API key, no PII, idempotent and safe to retry. The public endpoint https://mtgmon.com/mcp allows about 60 requests/min and 250 tool calls/day per client IP. Result shape is {data, meta}; meta carries available, as_of, source_scope, weighting, units, cache_status, caveats, and truncated. If meta.available is false the data is unavailable or the cache is still warming (retry after a few seconds); never report it as zero.
    get_lender_profileHMDA profile for one lender group: annual origination volume, loan counts, denial rate, national rank and market share, top states with market-share multiples, and loan type / purpose / occupancy / loan-size mix. Requires a canonical key: call search_lenders and pass the exact `name` (e.g. 'Rocket/Quicken'). Any other spelling returns meta.available=false with empty lists. Use compare_lenders for two lenders side by side. Returns data.lender_key, data.name, data.leis, data.current_year, data.previous_year, data.yearly (one row per HMDA year: originated_volume, originated_loans, purchased_volume, purchased_loans, denied_loans, applications_loans, total_funded_volume, avg_loan_size, denial_rate_pct, denial_rate_count_pct, rank, national_volume, market_share_pct), data.top_states {state, volume, loans, previous_volume, previous_loans} and data.state_market_multiples {state, lender_share_pct, market_share_pct, multiple, ...} (top_n rows each), data.loan_type_mix, data.loan_purpose_mix, data.occupancy_mix ({code, label, loans, volume}), and data.loan_size_histogram. Volumes are USD; shares and rates are percent. Behavior: read-only, no auth or API key, no PII, idempotent and safe to retry. The public endpoint https://mtgmon.com/mcp allows about 60 requests/min and 250 tool calls/day per client IP. Result shape is {data, meta}; meta carries available, as_of, source_scope, weighting, units, cache_status, caveats, and truncated. If meta.available is false the data is unavailable or the cache is still warming (retry after a few seconds); never report it as zero.
    compare_lendersSide-by-side HMDA comparison of two lender groups: yearly volume, loans, rank, and market share for both; loan type / purpose / occupancy mix; and the top states with each lender's volume and the volume multiple between them. Requires canonical keys for both lenders: call search_lenders and pass each result's exact `name`. For a single lender use get_lender_profile. Returns data.lenders, data.current_year, data.previous_year, data.yearly (both lenders per HMDA year), data.loan_type_mix, data.loan_purpose_mix, data.occupancy_mix, and data.top_state_comparison (top_n rows with lender_1_volume, lender_2_volume, multiple). Volumes are USD; shares are percent. Behavior: read-only, no auth or API key, no PII, idempotent and safe to retry. The public endpoint https://mtgmon.com/mcp allows about 60 requests/min and 250 tool calls/day per client IP. Result shape is {data, meta}; meta carries available, as_of, source_scope, weighting, units, cache_status, caveats, and truncated. If meta.available is false the data is unavailable or the cache is still warming (retry after a few seconds); never report it as zero.
    get_servicer_analyticsAgency (Fannie Mae / Freddie Mac) servicer analytics in one of three modes. - mode='scorecard': largest servicers ranked by disclosed origination UPB for the current and previous year with average FICO and LTV. Returns data.servicers (top_n rows of {rank, name, volume, previous_volume, avg_fico, avg_ltv}), data.current_year, data.previous_year. - mode='performance': monthly CPR and 90+ day delinquency per top servicer for one origination vintage against the whole-book benchmark. Returns data.servicers {name: {cpr: [{date, value}], dq90: [...]}} downsampled to max_points, data.book {cpr, dq90}, data.vintage, data.latest_date. - mode='transfers': where one originator's Fannie Mae loans are serviced today and how servicing moved from the seller. Requires `lender` (canonical key from search_lenders). Returns data.seller_name, data.summary {loan_count, origination_upb, loans_with_transfer, pct_loans_with_transfer, total_transfer_events, ...}, data.latest_active_servicers {servicer, loans, upb, upb_share_pct}, data.top_routes and data.top_named_routes {from_servicer, to_servicer, events, event_share_pct}, data.first_to_latest {first_servicer, latest_servicer, loans, original_upb} (top_n rows each). Use for: "largest servicers", "which servicer prepays fastest or has the worst DQ for the 2021 vintage", "who services Rocket's loans now". Do not use for: HMDA origination volume (get_lender_profile) or book-wide delinquency (get_loan_performance topic='delinquency'). Coverage is disclosed agency loans matched by seller/servicer name, not any servicer's full portfolio. UPB and volume are USD; CPR and DQ90 are percent. Behavior: read-only, no auth or API key, no PII, idempotent and safe to retry. The public endpoint https://mtgmon.com/mcp allows about 60 requests/min and 250 tool calls/day per client IP. Result shape is {data, meta}; meta carries available, as_of, source_scope, weighting, units, cache_status, caveats, and truncated. If meta.available is false the data is un
    get_dataset_statusCoverage, freshness, and build metadata for every mtgmon dataset. No arguments. Use for: "how current is the data", finding the latest available month or HMDA year before quoting figures, or diagnosing another tool's meta.available=false. Returns data.datasets, a list of {name, description, category, provider, coverage, last_updated, last_updated_label, stats, refresh} covering GSE loan performance, HMDA, market rates, SEC filings, and derived marts; and data.build {app, lake, cache_backend, cache}. Refresh cadences differ by dataset; read each row's coverage and last_updated. Behavior: read-only, no auth or API key, no PII, idempotent and safe to retry. The public endpoint https://mtgmon.com/mcp allows about 60 requests/min and 250 tool calls/day per client IP. Result shape is {data, meta}; meta carries available, as_of, source_scope, weighting, units, cache_status, caveats, and truncated. If meta.available is false the data is unavailable or the cache is still warming (retry after a few seconds); never report it as zero.
    search_sec_filingsSearch SEC EDGAR filings of mortgage-related public companies (lenders, servicers, mortgage REITs, insurers, GSEs) that mtgmon has summarized. Newest filing date first. Use for: finding a company's 10-K, 10-Q, 8-K, or other filings by ticker, CIK, form type, keyword, or date window, and getting the filing_id to pass to get_sec_filing. Do not use for: full filing text (only summaries are stored) or companies outside the mortgage sector. Returns data.filings (at most `limit` rows of {filing_id, accession_number, filer_cik, form, filing_date, company_name, description, summary_status, summary_excerpt, summary_created_at, edgar_url}) and data.next_cursor (pass back as `cursor` for the next page; null when exhausted; meta.truncated is true when more pages exist). Only filings whose summary processing finished are listed; summaries are machine-generated and should be checked against edgar_url. Behavior: read-only, no auth or API key, no PII, idempotent and safe to retry. The public endpoint https://mtgmon.com/mcp allows about 60 requests/min and 250 tool calls/day per client IP. Result shape is {data, meta}; meta carries available, as_of, source_scope, weighting, units, cache_status, caveats, and truncated. If meta.available is false the data is unavailable or the cache is still warming (retry after a few seconds); never report it as zero.
    get_sec_filingFull detail for one SEC filing: metadata and EDGAR link, mtgmon's machine-generated summary, which tracked mortgage entities the filing concerns and in what role, named people with titles and compensation, extracted financial facts, and ownership rows. Get the filing_id from search_sec_filings first. Returns data.filing {filing_id, accession_number, filer_cik, form, filing_date, company_name, description, canonical_summary, summary_status, summary_model, edgar_url, ...}, data.associations [{display_name, ticker, cik, relationship_role, summary, link_status, ...}], data.people [{name, title, compensation, note}], data.financials [{metric, value, unit, period}], and data.ownership. Each list is capped. Verify summaries against the linked filing before relying on them. Behavior: read-only, no auth or API key, no PII, idempotent and safe to retry. The public endpoint https://mtgmon.com/mcp allows about 60 requests/min and 250 tool calls/day per client IP. Result shape is {data, meta}; meta carries available, as_of, source_scope, weighting, units, cache_status, caveats, and truncated. If meta.available is false the data is unavailable or the cache is still warming (retry after a few seconds); never report it as zero.