Criteria - Revcenter API

Agents: the full documentation index is at /llms.txt.

A search runs exactly the criteria you send. Each field does one of two things:

Filter: sent to the LinkedIn search before any profile is read, so only people who match are considered.
Score: checked on each profile after it is read, and counted in the person’s score.

Filters are how you get the right people for the money; scoring is how they are ranked. Put a hard requirement in a filter field when there is one. The machine-readable version of this page is GET /v1/reference/criteria (or revcenter reference), with the allowed values for every field.

Minimum

title_variants (the role and its common synonyms) and a place: metro, allowed_metros, or remote_ok: true.

Filters

Field	Example	Notes
`title_variants`	`["Controller", "Financial Controller"]`	Current job titles. Up to 10 are searched together.
`metro`, `allowed_metros`	`"Lancaster, PA"`, `["Harrisburg, PA"]`	A city, a state, or a country.
`seniority`, `allowed_seniority`	`"director"`	`c_suite`, `vp`, `director`, `manager`, `ic`. Searched with one level either side.
`years_experience`	`{ "min": 8, "enforcement": "hard" }`	Hard ranges filter; `"preferred"` only ranks.
`years_at_current_company`	`{ "min": 3 }`	Time at the current employer.
`past_titles`	`["CFO"]`	Titles held before the current one.
`current_industries`	`["Food and Beverage Manufacturing"]`	The current employer’s LinkedIn industry, and its sub-industries. See `GET /v1/reference/industries`.
`exclude_industries`	`["Staffing and Recruiting"]`	Current employers in these industries are left out.
`functions`, `exclude_functions`	`["Finance"]`	LinkedIn job functions.
`company_headcount`	`["E", "F", "G"]`	Current employer size: `A` self-employed, `B` 1–10, `C` 11–50, `D` 51–200, `E` 201–500, `F` 501–1,000, `G` 1,001–5,000, `H` 5,001–10,000, `I` 10,001+.
`employers`	`[{ "name": "Deloitte", "temporal": "either", "flag": "must" }]`	`temporal`: `current`, `past` or `either`. `flag`: `must` filters, `exclude` leaves out, `prefer` only ranks.
`exclude_companies`	`["Acme Staffing"]`	Current employers to leave out.
`schools`, `exclude_schools`	`["University of Michigan"]`	Schools attended.
`exclude_titles`	`["Intern"]`	Current titles to leave out.
`exclude_locations`	`["New York, NY"]`	Places to leave out.
`recently_changed_jobs`	`true`	Changed jobs in the last 90 days.
`profile_languages`	`["es"]`	The language the profile is written in, not a language spoken.

Scoring

Field	Example	Notes
`must_have`	`["CPA", "PE license"]`	Each must be evidenced on the profile. Keep each to a short term; a sentence rarely matches anyone.
`skills`, `skills_required`	`["NetSuite"]`, `["CPA or Enrolled Agent"]`	`"A or B"` matches either.
`keywords`	`["SAP S/4HANA"]`	Words anywhere on the profile.
`languages_required`	`["Spanish"]`	Spoken languages.
`preferred_employers`	`["Big 4"]`	Ranks people there now or before higher; never excludes.
`geo_radius_mi`	`50`	Distance from `metro` for the location score.
`rubric_weights`	`{ "skills": 0.15 }`	How much each part counts: `title`, `geo`, `years`, `seniority`, `skills`, `industry`, each 0.02–0.5.
`shortlist_min_score`	`75`	The shortlist cut-off, 50–85. The default is 80.

Example

{
  "title_variants": ["Tax Manager", "Senior Tax Manager"],
  "metro": "Dallas, TX",
  "seniority": "manager",
  "years_experience": { "min": 7, "enforcement": "hard" },
  "employers": [{ "name": "Big 4", "temporal": "past", "flag": "must" }],
  "exclude_industries": ["Staffing and Recruiting"],
  "must_have": ["CPA"]
}