How to Use Main Street Index With MCP: What Small Businesses Sell For

search_mainstreet_listings

Filter businesses for sale by <strong>query</strong> (headline keyword), <strong>industry</strong> (slug, name or alias, e.g. laundromat), <strong>sector</strong>, <strong>country</strong>, <strong>state</strong>, <strong>currency</strong>, asking price, earnings and the seller&apos;s stated reason. Sort by newest, price, earnings or multiple, then pass <strong>next_cursor</strong> back to get the next page.

Example prompt: &apos;Find US laundromats for sale under $500k that disclose their SDE&apos;. The summary on every response states n, the price and earnings disclosure rates, and an estimated match count.

• earnings is SDE when earnings_basis is sde (US and Canada style) and net profit when it is net_profit (UK and Australia style). Never compare multiples across the two or across currencies.
• Each business is counted once: the same business listed on a second site is excluded unless include_duplicates is true.
• Numeric sorts only return listings that disclose that figure.
• Buyer filters: seller_financing (offered, open, not_offered), real_estate (owned, leased, lease_or_purchase), real_estate_included, management_model, training_offered, franchise_resale, home_based, relocatable, price_reduced (BizBuySell only, point in time), min_earnings_yield and vs_industry_band. Sort by yield_desc or vs_industry_asc. A listing that says nothing is not stated, never no.
• owner_involvement, sba_status and visa_eligible are read from the description by AI. All 14 AI fields passed their 85% accuracy gate on a fresh hand-checked sample (lowest: revenue_model, 86%), so they filter like any other field; each listing still carries the evidence quote.

semantic_search_mainstreet

Describe the business you want in plain language, for example &apos;absentee-run laundromat with a long lease&apos; or &apos;HVAC company with recurring maintenance contracts&apos;, and get the closest listings by meaning. Narrow it with country, industry, sector, currency or price.

get_mainstreet_industry

One industry in full: its definition, how many businesses are for sale, valuation benchmarks (asking price, earnings and multiple percentiles) split by currency and earnings basis, the Software Gap Score verdict (wide open, underserved or served) with every input behind it, the software supply mapped to the industry from Capterra, G2, Stripe Index and Upwork with each category&apos;s coverage, and the sellers&apos; stated reasons for selling.

Example prompt: &apos;What does a restaurant sell for in the US, and is the industry underserved by software?&apos;

• Percentiles are withheld below n=30 and in markets whose classification did not pass its accuracy gate. Those cells say is_indicative.
• Seller motivation is the stated reason in the listing text, not a verified cause, and non-answers are excluded from every rate.
• pct_pre_software is returned as unweighted context only, not as a per-industry figure.

get_mainstreet_listing

Pass the <strong>slug</strong> from any search result to get one business in full: asking price, revenue, SDE, net profit and FF&amp;E, each with its disclosed flag and band; years established, employees and square feet; location; reason for selling, lease, premises, trading hours, support and training, competition and expansion notes; the brokerage firm; the classified industry with the AI&apos;s confidence and reasoning; software exposure; the stated reason; and the asking-price history, one point per day.

A reworded headline still resolves, because the slug&apos;s suffix is stable. Price tracking began on 2026-09-20, so the history is short.

For buyers it also returns three blocks: <strong>buyer</strong> (seller financing and its terms, SBA label, management model, real estate and whether it is in the price, rent, lease expiry, inventory and FF&amp;E included, training, franchise, home based, relocatable and more, read from the site&apos;s own labels), <strong>deal</strong> (the asking multiple against its industry median for the same currency, basis and size band, with n, plus earnings yield and payback years) and <strong>buyer_ai</strong> (owner involvement, hours, manager in place, lease, equipment condition, revenue model, customer concentration, licences, visa and growth levers, each with the quote it was read from and its gate status).

get_mainstreet_coverage

Before you trust a per-country figure, check its coverage: what each listing site advertises versus what was scraped, its currency and earnings basis, disclosure rates for price, revenue and earnings, cross-site duplicates (each business is counted once), and whether the site passed its classification accuracy gate. UAE failed its gate and the small sites are indicative only.

rank_mainstreet_industries_for_buyers

Ranks industries for buyers by Buyer Fit, a score written down before any number was computed: median earnings yield (0.25), median asking price, lower is better (0.15), margin (0.15), years in operation (0.10), healthy-exit share of stated reasons (0.10), listings for sale (0.10), revenue per employee (0.05), seller-financing share (0.05) and owned-real-estate share (0.05). Each is a percentile rank inside one currency and earnings basis, so pounds are never pooled with dollars and SDE is never pooled with net profit.

Pass <strong>currency</strong> and <strong>earnings_basis</strong> (USD and sde by default), a budget as <strong>min_median_price</strong> / <strong>max_median_price</strong>, and an optional <strong>sector</strong>. Every row carries its rank and every input with its n. Example prompt: &apos;What are the best industries to buy into for under $500k?&apos;

• A group needs 10 or more rankable industries (each with 30+ listings disclosing price and earnings). Today only USD/SDE qualifies, so UK, Australian and Canadian rows come back withheld, with the reason, when include_withheld is true.
• It ranks asking-price economics and listing supply. It is not an investment recommendation, and every figure is an asking figure from listings, not a closed deal.

search_mainstreet_theses and get_mainstreet_brokers

search_mainstreet_theses returns software build theses for traditional industries that survived an independent adversarial judge, strongest first, each naming the system it attacks, the wedge and the evidence behind it.

get_mainstreet_brokers ranks brokerage firms by how many businesses they list, overall or within a source, industry or country. It is firm level only and never returns people or contact details. BizBuySell firm names are derived from URL slugs, so capitalization may be off.

FAQ