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POST
Search for People

Authorizations

Authorization
string
header
required

Pass your API key as a Bearer token. Keys follow the format ms_XXXXXXXXXXX.

Body

application/json
query
string

Full-text search across full_name, first_name, last_name, company name, title (~85% filled), and headline (~65% filled) using cross-field matching. Multi-word queries distribute terms across fields — "John Smith" matches first_name: John AND last_name: Smith rather than requiring either field alone to contain the full string. Job titles and role keywords are matched against both the title field (e.g. "Account Executive", "Head of Sales") and the headline field (e.g. "B2B sales leader"). For seniority level (VP, Director, C-suite) use the seniority filter instead — this field is best suited to specific role or function keywords, combined with other filters for precision, e.g. query: "Account Executive", country: "United States", seniority: "Senior". Categorical values like country or seniority belong in their own filter params rather than this field.

company
string

Company name filter — scopes results to people whose employer's name matches. Analysed match: every word you provide must appear in the company name, so extra words in the stored name are fine ("Acme" matches "Acme Corporation"). Use this when you have a company name but not its domain; if you have the domain, prefer company_domain, which is more precise.

Example:

"MoltSets"

company_domain
string

Company domain filter — scopes results to people at the company with this website domain. Full URLs are normalised automatically ("https://www.acme.com/about" → "acme.com"), so there is no need to strip the protocol or path yourself. The most precise way to target a single company — prefer it over company whenever you know the domain.

Example:

"moltsets.com"

country
string

Exact country name filter (~99% filled — most reliable filter). Must match the stored value exactly. Top values by volume: "United States", "India", "United Kingdom", "Brazil", "Canada", "France", "Mexico", "Australia", "China", "Spain", "Netherlands", "Italy", "Indonesia", "Germany", "Philippines", "Turkey", "South Africa", "Saudi Arabia", "Argentina", "Singapore", "United Arab Emirates", "Colombia", "South Korea", "Malaysia", "Poland", "Belgium", "Switzerland", "Ireland", "Sweden", "Denmark", "Norway", "Austria", "Portugal", "Israel", "New Zealand", "Finland", "Greece", "Hungary", "Romania", "Ukraine". Use the full English country name.

Example:

"United States"

state
string

Exact state or region name filter on the person's location. Must match the stored value exactly — full names with standard capitalisation, not abbreviations ("Texas", not "TX"). Combine with country for precision.

Example:

"Texas"

city
string

Exact city name filter on the person's location. Must match the stored value exactly, with standard capitalisation. City names repeat across regions, so combine with state and/or country to disambiguate — e.g. city: "Portland", state: "Oregon". Matches only the exact stored city, not its suburbs — for a metro-area search prefer state or country.

Example:

"Austin"

seniority
enum<string>

Exact seniority level filter (~60% filled). Must match one of the enumerated values exactly, including the space in "C Suite" (not a hyphen).

Available options:
Intern,
Entry,
Senior,
Manager,
Director,
VP,
Head,
C Suite,
Owner,
Partner
industry
enum<string>

Exact industry filter (~60% filled). Must match one of the enumerated values exactly, including capitalisation and punctuation (e.g. the ampersand in "Marketing & Advertising").

Available options:
Information Technology,
Professional and Business Services,
Finance and Banking,
Education,
Health and Pharmaceuticals,
Manufacturing,
Government and Public Administration,
Retail,
Food and Beverage,
Creative Arts and Entertainment,
Non-Profit and Social Services,
Transportation and Logistics,
Construction,
Tourism and Hospitality,
Energy,
Marketing & Advertising,
Telecommunications,
Real Estate,
Automotive,
Media and Publishing,
Agriculture
department
enum<string>

Exact department filter (~60% filled). Shares its underlying data with functional_area — use one or the other, not both. Must match one of the enumerated values exactly, including capitalisation and punctuation (e.g. the ampersand in "Medical & Health").

Available options:
Operations,
Sales,
Information Technology,
Education,
Engineering,
Finance,
Medical & Health,
Marketing,
Human Resources,
Design,
Consulting,
Legal
functional_area
enum<string>

Exact functional area filter (~60% filled). Shares its underlying data with department — use one or the other, not both.

Available options:
Operations,
Sales,
Information Technology,
Education,
Engineering,
Finance,
Medical & Health,
Marketing,
Human Resources,
Design,
Consulting,
Legal
employee_range
enum<string>

Exact employee-count range filter on the person's current employer — finds people at companies of a certain size without needing to name a specific company. Values are fixed bucket ranges (e.g. "51-200", "1001-5000"). Legacy values ("Small", "Mid-Market", "Enterprise", "Unknown") also exist in older records but have far less coverage than the numeric ranges. Combine with other filters, e.g. seniority: "VP", employee_range: "51-200".

Available options:
1-10,
11-20,
21-50,
51-200,
201-500,
501-1000,
1001-5000,
5001+,
Small,
Mid-Market,
Enterprise,
Unknown
revenue_range
enum<string>

Exact annual revenue range filter on the person's current employer — finds people at companies of a certain revenue without needing to name a specific company. Values are fixed bucket ranges. "$1 - $1M" is a legacy format equivalent to "$500k - $1M" but with far fewer records.

Available options:
Below $500k,
$500k - $1M,
$1M - $5M,
$5M - $10M,
$10M - $20M,
$20M - $50M,
Above $50M,
$50M - $100M,
$100M - $250M,
$250M - $500M,
$500M - $1B,
$1B - $2.5B,
$2.5B - $5B,
Over $5B,
$1 - $1M
naics_code
string

Exact NAICS industry code filter on the person's current employer (~50% filled). Works at any level of the hierarchy, from the 2-digit sector ("23" = Construction, "54" = Professional Services) through to the 6-digit national industry ("541120" = Offices of Notaries, "511210" = Software Publishers). Use a short code for broad industry targeting and a longer code for precision. The most standardised industry filter available — prefer it over industry when you need fine-grained targeting.

Example:

"541120"

linkedin_industry
string

Exact LinkedIn industry label filter on the person's current employer (~50% filled). Uses LinkedIn's own ~150-value industry vocabulary, which is far finer-grained than the 21 buckets in industry. Must match the stored label exactly, with standard capitalisation — for example "Software Development", "Hospitality", "Legal Services", "Wellness and Fitness Services", "Construction", "Staffing and Recruiting". Use when a niche industry cannot be expressed by the broad industry buckets.

Example:

"Software Development"

limit
integer
default:10

Number of results to return (default: 10, max: 25).

Required range: 1 <= x <= 25
offset
integer
default:0

Number of results to skip for pagination (default: 0). Use with limit to page through results — e.g. offset: 10 to get the second page of 10 results.

Response

Success