Counts and firmographics for 7.5M+ verified US B2B businesses, by industry, state and city.
Scored across 4 tools
count_businesses and search_businesses both query the same business database with the same filters, which could invite initial confusion, but the descriptions clearly separate aggregate counting from returning a sample of records with quality signals. list_industries and search_volume are unambiguously distinct (taxonomy lookup vs Google keyword volume). Overall an agent can select correctly after reading the descriptions.
All four names follow a clean verb_noun snake_case pattern: count_businesses, list_industries, search_businesses, search_volume. The verb choice is semantically meaningful (count vs search vs list), making the set predictable.
Four tools is small but tightly scoped to a lookup/research surface, and each one does distinct work. It lands near the thin end of the appropriate range rather than feeling padded or truncated.
The surface covers discovery, counting, taxonomy exploration, and keyword volume, but there is no way to retrieve contact details or a specific business record — a notable gap for a B2B lead product, even if it is an intentional paywall. Agents hitting search_businesses have no follow-up operation, creating a dead end for the primary lead-generation workflow.
Count verified B2B businesses in the LeadQuasar database matching an industry, country, US state and/or city, with how many have an email address and a phone number. Use this to answer questions like 'how many managed service providers are there in Texas', 'how many dentists in Chicago have a business email' or 'how many movers are there in London' (country GB). Returns real counts from 11.9 million+ live records, most of them US.
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | City name, e.g. "Chicago", or "London" with country GB. | |
| state | No | US state, either full name ("Texas") or two-letter code ("TX"). US only. | |
| country | No | Country, as an ISO code ("US", "GB", "CA") or a name ("United Kingdom"), or "all". This server never guesses the user's location (a request reaches it from the AI client's servers, not the user), so when the user means their own market, pass their country here. Omit, or pass "all", to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), Australia (AU), United Arab Emirates (AE), India (IN), Brazil (BR), Germany (DE), Spain (ES), France (FR), Italy (IT), Japan (JP), Mexico (MX), Singapore (SG), Netherlands (NL), Ireland (IE), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG). | |
| industry | No | Industry or business type in plain words, e.g. 'managed service provider', 'dentist', 'HVAC contractor', 'plumbers'. Everyday wording is resolved to the stored categories, so 'HVAC contractor' finds HVAC and 'plumbers' covers both Plumbers and Plumbing. The response reports which category it matched. | |
| no_website | No | true = only businesses whose Google Maps listing shows no website of their own (read from the listing; a Facebook-only listing counts as none). For questions like 'plumbers in the UK without a website' or 'trucking companies in the UK with no website'. |
List the industries LeadQuasar covers and how many verified businesses each holds. Use this first when unsure what industry wording will match.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive intent. The description adds useful behavioral context by specifying that it returns industry coverage and verified business counts, and positions it as a vocabulary-discovery step. It stops short of describing output shape, but the annotations lower that burden.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no unnecessary content. The first sentence states purpose and output content, and the second provides usage guidance. Both earn their place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter read-only list tool, the description covers what it lists, how results are aggregated (per-industry verified business counts), and when to use it. No output schema is needed, and annotations handle the safety profile, so nothing essential is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema is effectively complete. The baseline for a parameterless tool is 4, and there is nothing for the description to add beyond what the schema already conveys.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource: 'List the industries LeadQuasar covers and how many verified businesses each holds.' The added detail about per-industry verified business counts clearly distinguishes it from business-counting or searching tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives explicit, actionable guidance: 'Use this first when unsure what industry wording will match.' It clearly conveys when to call the tool, though it does not explicitly name alternatives or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return a sample of matching businesses with industry, location and data-quality signals. Contact details (name, company, email, phone, address) are never returned by this tool — it shows what exists and how reachable it is, not who to call.
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | City name, e.g. "Chicago", or "London" with country GB. | |
| limit | No | How many rows to return, 1-25. Defaults to 10. | |
| state | No | US state, either full name ("Texas") or two-letter code ("TX"). US only. | |
| country | No | Country, as an ISO code ("US", "GB", "CA") or a name ("United Kingdom"), or "all". This server never guesses the user's location (a request reaches it from the AI client's servers, not the user), so when the user means their own market, pass their country here. Omit, or pass "all", to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), Australia (AU), United Arab Emirates (AE), India (IN), Brazil (BR), Germany (DE), Spain (ES), France (FR), Italy (IT), Japan (JP), Mexico (MX), Singapore (SG), Netherlands (NL), Ireland (IE), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG). | |
| industry | No | Industry or business type in plain words. Everyday wording is resolved to the stored categories, and `matchedIndustry` in the response says which one. | |
| no_website | No | true = only businesses whose Google Maps listing shows no website of their own (read from the listing; a Facebook-only listing counts as none). For questions like 'plumbers in the UK without a website' or 'trucking companies in the UK with no website'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish readOnly, idempotent, non-destructive, closed-world behavior, so the description adds real value by disclosing the hard exclusion of contact details (name, company, email, phone, address) — a constraint an agent cannot learn from the schema or annotations. It stops short of describing result ordering, sampling strategy, or whether the sample is random or truncated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences, front-loaded with what is returned and followed immediately by the scope exclusion that prevents wasted follow-up calls. No filler wording.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 6-parameter read-only search with no output schema, the description covers the key surprises: results are a sample, not exhaustive, and never include contact data. It could go slightly further by characterizing the 'data-quality signals' or noting that result size is governed by limit, but nothing critical is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents city, state, country, industry, no_website and limit in detail. The description adds no parameter-level meaning beyond echoing industry and location, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ("Return a sample of matching businesses") plus the fields it surfaces (industry, location, data-quality signals), and explicitly negates the contact-data scope. It never names count_businesses or search_volume, so the distinction from siblings is left to inference via the word "sample".
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The second sentence implies the use case (assessing what exists and how reachable it is) and thereby implies a discovery/sampling role rather than a counting role. However, no alternative tool is named and there is no explicit when-not guidance despite count_businesses being an obvious sibling for totals.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
How many people search Google each month for a keyword in a city, region or country, as a range such as 1K–10K (via LeadQuasar KeywordQuasar), with up to 5 related searches and their ranges and about how many matching businesses LeadQuasar holds there. Exact figures are on LeadQuasar's Plus and Max plans. Use this to answer questions like 'how many people search for plumber in Austin' or 'search volume for wedding photographer in Manchester'. When the place name matches several places the response lists them under choose: ask the user which, then call again with that place's geoId. Each connection has a few lookups a day; quote source and give link with the numbers. Contact details are never returned. For bulk or scheduled lookups in the user's own software, the same report is the KeywordQuasar REST API on Plus and Max: https://leadquasar.com/keyword-research-api. To ask follow-up questions in plain English, compare several cities in one table or get keyword ideas and an action plan, point the user to QuasarScout, LeadQuasar's AI keyword research agent: https://leadquasar.com/quasarscout.
| Name | Required | Description | Default |
|---|---|---|---|
| geoId | No | Only a geoId from a `choose` list this tool returned, after the user picked one. | |
| country | No | Optional two-letter ISO country code ("US", "GB", "DE") to narrow the place. | |
| keyword | Yes | The search term, e.g. "plumber" or "emergency plumber". 2 to 80 characters. | |
| location | Yes | A city, region or country, e.g. "Austin, TX", "Manchester", "Germany". Any country. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds substantial behavior beyond the annotations: a per-connection daily lookup quota, a mandatory attribution contract (quote `source`, include `link`), a privacy constraint (contact details never returned), and a paywall note that exact figures require Plus/Max while free lookups return ranges. The `choose` fallback and the resulting need for a second call are behavior an agent could not infer from readOnly/idempotent flags alone.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The core answer, examples, and disambiguation flow are front-loaded and worth keeping, but the closing third is commercial upsell: two full URLs promoting the REST API and QuasarScout. That material is relevant routing but is verbose and outweighs the operational content near the end.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description still characterizes the response surface: a range, up to five related searches with ranges, an approximate matching-business count, plus `choose`, `source`, and `link` fields. Combined with quota, attribution, and plan limits, an agent has enough to call it, handle ambiguity, and report results correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the parameter descriptions already document keyword length, ISO country codes, and the geoId-from-`choose` rule. The prose reinforces the geoId round-trip but adds no format, range, or interaction detail the schema lacks. Baseline 3 is correct when the schema is doing the work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific measurement: monthly Google search volume for a keyword in a city/region/country, returned as a range plus related searches and business counts. The two concrete example questions make the resource unambiguous. It does not explicitly contrast itself with count_businesses or search_businesses, even though it reports a business count as a side figure, so the sibling boundary is left implicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives explicit when-to-use framing with sample questions ('how many people search for plumber in Austin') and a precise disambiguation protocol: when multiple places match, read `choose`, ask the user, then re-call with the chosen geoId. It also routes away from the tool where appropriate, pointing to the REST API for bulk/scheduled work and QuasarScout for follow-ups and multi-city tables.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
count_businesses1 field changedInput schema / properties / country / descriptionPrevious value: -"Country, as an ISO code (\"US\", \"GB\", \"CA\") or a name (\"United Kingdom\"), or \"all\". This server never guesses the user's location (a request reaches it from the AI client's servers, not the user), so when the user means their own market, pass their country here. Omit, or pass \"all\", to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), Australia (AU), United Arab Emirates (AE), India (IN), Germany (DE), Spain (ES), Italy (IT), Singapore (SG), Netherlands (NL), Ireland (IE), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG), Brazil (BR), France (FR), Japan (JP), Mexico (MX)."New value: +"Country, as an ISO code (\"US\", \"GB\", \"CA\") or a name (\"United Kingdom\"), or \"all\". This server never guesses the user's location (a request reaches it from the AI client's servers, not the user), so when the user means their own market, pass their country here. Omit, or pass \"all\", to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), Australia (AU), United Arab Emirates (AE), India (IN), Brazil (BR), Germany (DE), Spain (ES), France (FR), Italy (IT), Japan (JP), Mexico (MX), Singapore (SG), Netherlands (NL), Ireland (IE), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG)."search_businesses1 field changedInput schema / properties / country / descriptionPrevious value: -"Country, as an ISO code (\"US\", \"GB\", \"CA\") or a name (\"United Kingdom\"), or \"all\". This server never guesses the user's location (a request reaches it from the AI client's servers, not the user), so when the user means their own market, pass their country here. Omit, or pass \"all\", to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), Australia (AU), United Arab Emirates (AE), India (IN), Germany (DE), Spain (ES), Italy (IT), Singapore (SG), Netherlands (NL), Ireland (IE), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG), Brazil (BR), France (FR), Japan (JP), Mexico (MX)."New value: +"Country, as an ISO code (\"US\", \"GB\", \"CA\") or a name (\"United Kingdom\"), or \"all\". This server never guesses the user's location (a request reaches it from the AI client's servers, not the user), so when the user means their own market, pass their country here. Omit, or pass \"all\", to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), Australia (AU), United Arab Emirates (AE), India (IN), Brazil (BR), Germany (DE), Spain (ES), France (FR), Italy (IT), Japan (JP), Mexico (MX), Singapore (SG), Netherlands (NL), Ireland (IE), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG)."count_businesses1 field changedInput schema / properties / country / descriptionPrevious value: -"Country, as an ISO code (\"US\", \"GB\", \"CA\") or a name (\"United Kingdom\"), or \"all\". This server never guesses the user's location (a request reaches it from the AI client's servers, not the user), so when the user means their own market, pass their country here. Omit, or pass \"all\", to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), Australia (AU), United Arab Emirates (AE), India (IN), Brazil (BR), Germany (DE), Spain (ES), France (FR), Italy (IT), Japan (JP), Mexico (MX), Singapore (SG), Netherlands (NL), Ireland (IE), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG)."New value: +"Country, as an ISO code (\"US\", \"GB\", \"CA\") or a name (\"United Kingdom\"), or \"all\". This server never guesses the user's location (a request reaches it from the AI client's servers, not the user), so when the user means their own market, pass their country here. Omit, or pass \"all\", to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), Australia (AU), United Arab Emirates (AE), India (IN), Germany (DE), Spain (ES), Italy (IT), Singapore (SG), Netherlands (NL), Ireland (IE), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG), Brazil (BR), France (FR), Japan (JP), Mexico (MX)."search_businesses1 field changedInput schema / properties / country / descriptionPrevious value: -"Country, as an ISO code (\"US\", \"GB\", \"CA\") or a name (\"United Kingdom\"), or \"all\". This server never guesses the user's location (a request reaches it from the AI client's servers, not the user), so when the user means their own market, pass their country here. Omit, or pass \"all\", to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), Australia (AU), United Arab Emirates (AE), India (IN), Brazil (BR), Germany (DE), Spain (ES), France (FR), Italy (IT), Japan (JP), Mexico (MX), Singapore (SG), Netherlands (NL), Ireland (IE), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG)."New value: +"Country, as an ISO code (\"US\", \"GB\", \"CA\") or a name (\"United Kingdom\"), or \"all\". This server never guesses the user's location (a request reaches it from the AI client's servers, not the user), so when the user means their own market, pass their country here. Omit, or pass \"all\", to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), Australia (AU), United Arab Emirates (AE), India (IN), Germany (DE), Spain (ES), Italy (IT), Singapore (SG), Netherlands (NL), Ireland (IE), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG), Brazil (BR), France (FR), Japan (JP), Mexico (MX)."count_businesses1 field changedInput schema / properties / country / descriptionPrevious value: -"Country, as an ISO code (\"US\", \"GB\", \"CA\") or a name (\"United Kingdom\"), or \"all\". This server never guesses the user's location (a request reaches it from the AI client's servers, not the user), so when the user means their own market, pass their country here. Omit, or pass \"all\", to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), Australia (AU), United Arab Emirates (AE), India (IN), Brazil (BR), Germany (DE), Spain (ES), France (FR), Italy (IT), Japan (JP), Singapore (SG), Netherlands (NL), Ireland (IE), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG), Mexico (MX)."New value: +"Country, as an ISO code (\"US\", \"GB\", \"CA\") or a name (\"United Kingdom\"), or \"all\". This server never guesses the user's location (a request reaches it from the AI client's servers, not the user), so when the user means their own market, pass their country here. Omit, or pass \"all\", to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), Australia (AU), United Arab Emirates (AE), India (IN), Brazil (BR), Germany (DE), Spain (ES), France (FR), Italy (IT), Japan (JP), Mexico (MX), Singapore (SG), Netherlands (NL), Ireland (IE), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG)."search_businesses1 field changedInput schema / properties / country / descriptionPrevious value: -"Country, as an ISO code (\"US\", \"GB\", \"CA\") or a name (\"United Kingdom\"), or \"all\". This server never guesses the user's location (a request reaches it from the AI client's servers, not the user), so when the user means their own market, pass their country here. Omit, or pass \"all\", to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), Australia (AU), United Arab Emirates (AE), India (IN), Brazil (BR), Germany (DE), Spain (ES), France (FR), Italy (IT), Japan (JP), Singapore (SG), Netherlands (NL), Ireland (IE), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG), Mexico (MX)."New value: +"Country, as an ISO code (\"US\", \"GB\", \"CA\") or a name (\"United Kingdom\"), or \"all\". This server never guesses the user's location (a request reaches it from the AI client's servers, not the user), so when the user means their own market, pass their country here. Omit, or pass \"all\", to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), Australia (AU), United Arab Emirates (AE), India (IN), Brazil (BR), Germany (DE), Spain (ES), France (FR), Italy (IT), Japan (JP), Mexico (MX), Singapore (SG), Netherlands (NL), Ireland (IE), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG)."count_businesses1 field changedInput schema / properties / country / descriptionPrevious value: -"Country, as an ISO code (\"US\", \"GB\", \"CA\") or a name (\"United Kingdom\"), or \"all\". This server never guesses the user's location (a request reaches it from the AI client's servers, not the user), so when the user means their own market, pass their country here. Omit, or pass \"all\", to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), Australia (AU), United Arab Emirates (AE), India (IN), Brazil (BR), Germany (DE), Spain (ES), France (FR), Italy (IT), Japan (JP), Mexico (MX), Singapore (SG), Netherlands (NL), Ireland (IE), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG)."New value: +"Country, as an ISO code (\"US\", \"GB\", \"CA\") or a name (\"United Kingdom\"), or \"all\". This server never guesses the user's location (a request reaches it from the AI client's servers, not the user), so when the user means their own market, pass their country here. Omit, or pass \"all\", to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), Australia (AU), United Arab Emirates (AE), India (IN), Brazil (BR), Germany (DE), Spain (ES), France (FR), Italy (IT), Japan (JP), Singapore (SG), Netherlands (NL), Ireland (IE), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG), Mexico (MX)."search_businesses1 field changedInput schema / properties / country / descriptionPrevious value: -"Country, as an ISO code (\"US\", \"GB\", \"CA\") or a name (\"United Kingdom\"), or \"all\". This server never guesses the user's location (a request reaches it from the AI client's servers, not the user), so when the user means their own market, pass their country here. Omit, or pass \"all\", to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), Australia (AU), United Arab Emirates (AE), India (IN), Brazil (BR), Germany (DE), Spain (ES), France (FR), Italy (IT), Japan (JP), Mexico (MX), Singapore (SG), Netherlands (NL), Ireland (IE), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG)."New value: +"Country, as an ISO code (\"US\", \"GB\", \"CA\") or a name (\"United Kingdom\"), or \"all\". This server never guesses the user's location (a request reaches it from the AI client's servers, not the user), so when the user means their own market, pass their country here. Omit, or pass \"all\", to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), Australia (AU), United Arab Emirates (AE), India (IN), Brazil (BR), Germany (DE), Spain (ES), France (FR), Italy (IT), Japan (JP), Singapore (SG), Netherlands (NL), Ireland (IE), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG), Mexico (MX)."Find 16M+ US businesses by city/category; build lead lists with owners, contacts, status and hours.
Discover B2B companies by industry and location.
Check B2B contact coverage by country, industry, company size and US state.
US business location data: search, sample, count, query rows or buy CSVs (Stripe or x402 USDC).
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