Data Sources and Talent Pools Available in SeekOut MCP
SeekOut MCP connects your AI assistant to five specialized talent pools and your ATS pipeline. Learn which data source to use and when.
SeekOut MCP gives your AI assistant access to six distinct talent data sources — five curated talent pools and your organization's own ATS pipeline. You don't need to specify which source to use; the assistant reads your query and automatically selects the most relevant pool. Ask about nurses and it searches the Nursing database. Ask about open-source contributors and it pulls from GitHub. You can also be explicit when you want precise control.
How the AI Picks a Data Source
When you submit a query, the assistant analyzes the role, skills, and context you've described and routes the search to the best-fit data source. If your query spans multiple pools — for example, a software engineer with published research — the assistant will surface that ambiguity and let you choose.
You can also specify a source directly:
Search the GitHub data source for Rust engineers in Berlin with active open-source contributions.Talent Pool Data Sources
| Data source | What it covers |
|---|---|
| Public Profiles Default | Aggregated professional data — employers, job titles, experience, skills, education, and location. The starting point for most searches. |
| GitHub | Software engineers surfaced through open-source contributions, programming languages, repository activity, and community engagement — not self-reported skills alone. |
| Academic & Expert | Researchers and subject-matter experts, filterable by h-index, citations, papers, patents, and research areas. |
| Healthcare | Physicians and clinical professionals from NPI registry data, with specialty and hospital-affiliation information. |
| Nursing | RNs, LPNs, and nurse practitioners, searchable by credential type, license status, and state licensure. |
ATS Pipeline
Available when your organization has a connected ATS such as Greenhouse or Lever. Search your existing candidate pipeline in plain language — by stage, application date, and recruiter notes — right alongside external talent, so you check who you already have before sourcing anew.
Example Queries — ATS Pipeline
Show me everyone in our Senior Backend Engineer pipeline who's been waiting more than two weeks.Who applied to our Product Designer role in the last month?Find past applicants for data engineering roles we never moved forward.Example Queries by Data Source
| Data source | Example query |
|---|---|
| Public Profiles | "Senior product managers at fintech companies in NYC" |
| GitHub | "Top Rust contributors with systems programming experience" |
| Academic & Expert | "AI researchers with 50+ citations on transformer architectures" |
| Healthcare | "Cardiologists affiliated with top academic medical centers" |
| Nursing | "ICU nurses with active licenses in Illinois" |
Tips for Better Results
- Be specific about location and seniority
- Name the data source when precision matters
- Use natural language rather than boolean syntax
- Check your ATS pipeline before sourcing externally
- Refine results through follow-up questions — the assistant keeps your search context