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name: anysite-people-sourcing description: Source people in bulk from Anysite's LinkedIn profile database (search_sql_users) with ~60 filters - derived seniority/function, company domain/id incl. past employers (alumni), career-shape (new in role, tenure, promotions), education, skills, deterministic territory buckets. Use when the user wants to find people in bulk - persona lists, buyers/candidates by role and company traits, competitor alumni, "new VP hires", recruiting searches - "найди людей", "собери список персон", "кто недавно сменил работу". For company lists first use anysite-company-sourcing; for one known person use linkedin/user; for CRM push use anysite-crm-prospect.

People Sourcing

linkedin/search/sql/users (endpoint search_sql_users) searches a very large profile database with filters no live LinkedIn search has: derived seniority and function, career-shape maths (tenure, promotions, months in role), past-employer alumni, company domains and deterministic bucketing. It is the bulk people workhorse; live search_users stays the tool for one-off lookups and namesake disambiguation.

Verified live: filters compose correctly (seniority_min=head + function=sales + US + 51–500 headcount + new-in-role returned exactly that, role histories fresh to the current quarter; alumni via past_company_id returns people whose role at that company has an end date, never current staff).

Two semantics that change how you work

1. An over-count result is an unbiased SAMPLE, and repeating the request returns the SAME people (verified: two identical calls → identical 5 aliases; and raising count only appends — the list is prefix-stable, not reshuffled). Calling again is not pagination.
2. Buckets are disjoint and stable (verified: bucket_total:2, index 0 vs 1 → zero overlap across 40). Walk them for volume: bucket_total:N, bucket_index:0..N-1, also the built-in territory splitter. Ceiling: bucket_total max is 100, so the reachable population is ~100×1000 = 100k; size bucket_total so each bucket is < 1000, otherwise a bucket is itself a sample and you silently lose the tail.

What filters but does NOT come back (read before you "validate")

The derived fields you filter on are not in the response: no seniority, function, profile_score, months_in_role, promotion_count, industry, employee_count (verified on a live record). Consequences:

• "Validate against intent" = read experience[] by hand: take roles with end_date == null (the current ones) and check .position / .company.name. You cannot re-sort or re-segment the result by seniority/function via query_cache — those columns aren't there.
• The only size field carried per role is experience[].company.employee_range, and that string is unreliable (see Company filters). There is no trustworthy company-size value in a people result to write to a CRM.

Filter map (grouped; full contract via discover(linkedin, search))

• Batch identity: alias[], urn[], member_id[]; last_name[] (diacritics/case/spacing-folded) + first_initial[] for "J. Smith".
• Text DSL (whitespace=AND, |=OR no spaces around it, "phrase", -not): name, headline, summary, current_title, any_title (past roles included), skills, languages, location, keywords, edu_*. IRON RULE — quote every multi-word alternative in an OR chain. Whitespace binds tighter than |, so an unquoted phrase SHATTERS the chain into impossible AND groups: current_company_name: "Tencent|Tencent Games|Level Infinite|Proxima Beta" parses as (Tencent|Tencent) AND (Games|Level) AND (Infinite|Proxima) AND Beta → 0 results on Tencent-scale companies (verified live — this exact failure burned a real user session). Correct: "Tencent"|"Tencent Games"|"Level Infinite"|"Proxima Beta"|Krafton. Lint before EVERY send: any token containing a space inside an OR chain without quotes → do not send, fix first.
• Derived seniority/function — start here for recall. seniority / seniority_min (entry→ic→senior_ic→manager→head→vp→founder→cxo), function / any_function (sales, marketing, engineering, product, data, finance, hr, ops, legal, support, exec, …). Catch titles a text search can't (CRO, CCO, "VP Business Development" all matched a function:sales+seniority_min:vp query — none would survive a "vp of sales" DSL). Two traps, both measured:
  • seniority_min:"head" includes founder → pulls solo owners of 2-person shops. For enterprise buyers list the levels explicitly: seniority:["head","vp","cxo"], and always add employee_count_min.
  • Token false-positives: "CEO Office" (staff, not the CEO) scored ≥head; "…President's Club 2020…" (an award on a rank-and-file AE) scored ≥vp. Precision on a vp+sales slice was 9/10 — good, not perfect; verify the shortlist against experience[].
• Company — a ladder, pick the rung deliberately:
  1. current_company_name DSL with QUOTED brand variants — the default. A bare token like Tencent also matches subsidiaries ("Tencent Korea", "Fintech at Tencent") — a feature for brand families, a bug if you need one legal entity.
  2. current_company_id[] (numeric LinkedIn page ids, resolve via company search first) — when strictness matters: separating Level Infinite from the rest of Tencent, excluding brand-name lookalikes.
  3. current_company_domain[] (bare domains) — when domains are what you have (e.g. a CRM list).
  4. keywords — LAST resort, only WITH ≥1 selective filter (company/title/ seniority/country) and never as the employer filter itself: it's full-text across all fields, and an unselective keywords query is a DB scan (measured: 140s → HTTP 500). Also: any_company_id/domain (ever worked); past_company_id[] (worked and LEFT — alumni); employee_count_min/max of the current company. Do NOT use employee_range[] — the band string contradicts the true headcount often (measured ~35%: a 104-person company banded "11-50", a 58-person one "501-1000"); filter size only with the numeric employee_count_min/max.
• Industry: industry[] / any_industry[] take exact LinkedIn-taxonomy labels (e.g. "Software Development" works), NOT market words — ["fintech"] returns 0 (measured). Resolve labels via search_industries; for "fintech", "SaaS" and the like, filter on the company side (anysite-company-sourcing specialities) or on headline/skills DSL instead.
• Geo: country[] ISO2 (reliable); location DSL for cities/metros. "Responsible for a market" ≠ "located in it." When the user wants people FOCUSED on a region (CIS/MENA/emerging markets), do NOT filter country by that region — BD/publishing teams of global companies sit in HQ (KR/CN/US/NL/ SG). Search the market mention in the profile instead: headline/summary: CIS|Russia|Kazakhstan|MENA|"emerging markets"|"Middle East". Measured on a live case: country:[RU,KZ,UZ,AE,SA,…] → 2 profiles; the headline variant → 84. Use country only when the person must physically BE in the region — and warn the user the pool will be narrow.
• Education: edu_slug[], edu_institution, edu_field, edu_ended_year_min/max (graduation cohort ≈ age proxy).
• Career shape: months_in_role_max (new in role), months_in_role_min, months_since_change_max (started OR ended a role recently — job-change radar), experience_years_min, n_roles_*, n_companies_* (stability vs hopping), promotion_count_min (promoted without changing employer), avg_tenure_months_*.
• Quality & coverage: profile_score_min (0–8) — do NOT set it by default: it silently drops sparse-but-on-target profiles (a real user's default of 5 cut exactly the people they were hunting). Add it only when the user asks for "complete/outreach-grade profiles" (then 5+), or as a tie-breaker on an over-1000 pool. Plus the has_* family (has_current_role, has_role_dates, has_education, has_company_size, has_engagement…).
• Badges/engagement: open_to_work, hiring, verified, is_premium, is_top_voice, follower_count_min, connection_count_min. (is_creator / is_influencer filter but are NOT returned — you can't confirm them.)
• Lookalike (EXPERIMENTAL — verify every time): similar_to / also_viewed by alias. Measured unreliable: two seeds returned namesakes (same name, different people) or nothing — NOT semantic lookalikes. Do not use it as a "people like our champion" shortcut; if you try it, probe count:5 and confirm the surnames differ from the seed's before trusting a single row. For real lookalikes, replicate the champion's attributes as explicit filters instead.
• Freshness: fetched_after (unix ts) — only recently refreshed profiles.

Coverage honesty

Not every profile states a current employer, industry, company size or dated roles — those filters answer only for people who do. The API's own numbers: current title, seniority and function resolve for about a third of people; current_company_id, current_company_domain and employee_range for about one person in five. So a company-id or domain rung returns roughly a fifth of the people who really work there — say so, and for coverage run the current_company_name rung alongside it. Set the matching has_* flag deliberately and tell the user the trade-off ("filtering by company size skips people whose company doesn't state one"). open_to_work: false = "no badge observed", not "not looking". Follower/connection counts exist on a small share — gate with has_engagement.

Two data-shape facts to expect: some profiles carry multiple concurrent open roles (measured: 4/10 in one slice; one person had ~19 — advisory boards, visiting posts). "Current company" is then ambiguous, and a current_company_domain/employee_count filter may match on a quarter-time advisory role, not the day job — so verify the target company is an operational current role before anchoring an opener on it. And urn is occasionally null even when alias is present — the vanity URL still enables user_find_email_by_url and user_posts (it accepts the alias/URL and resolves it at the cost of one extra lookup).

The loop

1. Start from the most selective structural filters (seniority/function/ company/country) — never bare keywords (it only helps for distinctive words and must ride a selective filter). No persona in the request → use the titles, seniority and function from the Personas section of anysite-gtm-profile when it exists.
2. Probe count:10, validate against INTENT via experience[] (structural filters aren't semantic: "Director of Sales, 51–200, US" returned a coffee wholesaler). Precision comes from current_company_domain[] (a list from anysite-company-sourcing), taxonomy industry[], or headline/skills DSL.
3. Tighten/widen, re-probe, fetch; >1000 → size bucket_total so each bucket < 1000 and walk it.
4. Free re-cuts with query_cache (only on returned fields); export with export_data. In clients that render MCP Apps, show the result with show_entity_table(cache_key, group_by="company_id") so people are grouped by account; people at approved accounts from review_leads start from that selection's company ids.

Zero results — debug in this order, don't flail:

1. Re-read your own DSL for unquoted multi-word phrases in OR chains (the #1 cause by far — see the IRON RULE).
2. Loosen ONE filter per step (drop profile_score_min, widen seniority, drop industry) — never jump straight to a bare keywords full-text.
3. Sanity check: 0 results for a Tencent-scale employer is almost certainly a query bug, not missing data — go back to step 1, don't report "not found".

500/408 on a broad query: don't retry as-is — narrow it (add a selective filter) or raise timeout (≤1500). An unselective query is a scan; retrying a scan just times out again.

Recipes

• "VPs of Sales at US SaaS 50–500": seniority:["vp","cxo"], function:["sales"], country:["US"], employee_count_min:50, employee_count_max:500, industry:["Software Development"]. (SaaS ≠ a taxonomy label — narrow via the company-side list or headline DSL. profile_score_min only if asked.)
• "BD/partnerships across a brand family" (verified live, 99 profiles): current_company_name: "\"Tencent\"|\"Tencent Games\"|\"Level Infinite\"| \"Proxima Beta\"|Krafton", current_title: "\"business development\"|bizdev| partnership|partnerships|licensing|monetization|payments|commercial", seniority:["manager","head","vp","cxo"], has_current_role:true — every multi-word alternative quoted; regional focus goes in headline, not country (see Geo).
• "New decision-makers" (best-converting timing): add months_in_role_max:3 (default 90 days). Too few → widen the title family first, then relax size or geography, and only then the window (6 months; 9 is the ceiling).
• "Buying committee at one account": anysite-buying-committee.
• "Warm paths": people who used to work at your customers — past_company_id:[<customer ids>] + your persona filters; name the shared employer in the message only if the user confirms it may be mentioned.
• "Grew up inside the company" (internal champion / stable ABM contact): promotion_count_min:2 + avg_tenure_months_min:24.
• "Competitor alumni": past_company_id:[<ids>] (+ function/seniority) — people who LEFT. Note: this is ex-employees, NOT the competitor's customers; for customers use anysite-crm-competitor-intel (wappalyzer + reviews).
• EMEA localized sequences: languages:"german", country:["DE","AT","CH"].
• School alumni warm-intro: edu_slug:[...] + edu_ended_year_min/max.
• Recruiting: skills/edu_field DSL + avg_tenure_months_min (stability) + n_companies_max (not a hopper) + open_to_work:true as a bonus tier only.
• Territory split: same query, bucket_total:<reps>, bucket_index:<rep #>.

Handoffs and hygiene

• Company lists IN (current_company_id/domain) come from anysite-company-sourcing; people OUT go to anysite-crm-prospect (dedup + CRM push).
• For "up to N contacts across a few companies", 2–3 focused queries (base + regional + function-specific) deduped by alias beat one mega-query — each stays selective and each angle surfaces people the others rank low.
• Emails have a real cost and yield — don't hand-wave it. The result's alias is the vanity URL, ready for the email cascade (anysite-mcp → Email finding): user_email first (cheap, ~half personal addresses, low yield), then user_find_email_by_url (~75% yield but 50cr each — estimate 50cr × N and confirm before a big run) with its valid_email/email_status as the deliverability gate. Personal/unverified addresses never go into a work sequence. For bounce-critical sends, run the address through emails/verify (status valid / invalid / risky, is_personal); it may answer from a verdict stored up to a year ago (resolved_by says which).
• Live-verify the outreach shortlist via linkedin/user with a small cache_max_age_days (e.g. 7) — without it the call may return a profile up to 180 days old, which defeats the check; it also yields the vanity URL. Job-change detection on CRM contacts belongs to anysite-crm-champions (this DB's months_since_change_max is its cheap pre-filter, not its evidence).
• Sourcing individuals is personal-data processing: search to the stated business need, don't hoard.