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QueryQuarry MCP Reference

QueryQuarry exposes a consent-based talent graph over the Model Context Protocol (MCP). Connect it to your AI assistant and search, evaluate, and reach out to candidates in natural language — the recruiter's AI is the intelligence; QueryQuarry is the corpus.

Connecting

QueryQuarry's MCP server lives at https://queryquarry.com/api/mcp. There are two ways to authenticate:

  • OAuth — add QueryQuarry as a connector in an MCP-capable client (Claude, ChatGPT, etc.) and authorize it. Best for interactive use.
  • API key — send an Authorization: Bearer qq_… header. Best for scripts and custom integrations. Generate and manage keys in your recruiter dashboard.

Either way you act as one recruiter account, with your subscription tier and limits applied server-side. To create an account, start at recruiter sign-up.

How it works

QueryQuarry is a double-blind escrow marketplace. Identities are revealed last, only by mutual, deliberate action:

  1. Search the graph — you get anonymous match cards (no name, no contact).
  2. Evaluate a promising card with a deeper, still-anonymous profile.
  3. Request contact — you identify yourself; the candidate is notified.
  4. The candidate decides. If they're interested, they reach out to you directly, quoting a one-time code so you know it's really them.

The candidate's name and contact details are never handed to you by the platform — they stay private until the candidate chooses to respond. That consent step is what keeps the corpus opt-in and spam-free.

Tools

Tool What it does
search_candidates Search the talent graph. Returns anonymous match cards — headline, skills, seniority, location, availability, salary range, and a snippet — but no name or contact. Filter by skills, location, remote, seniority, employment type, salary, experience, work authorization, relocation, and more. Paginated (max 25/page).
get_candidate Evaluate one candidate in depth. Full skills, summary, seniority, experience (titles + what they did) and education — but still anonymous: no name or contact, and the candidate's name and contact details are scrubbed from the text. Metered (counts toward your reveal limit).
request_contact Reach out to one candidate. You identify yourself (name + company, from your account) and add a short message; the candidate is notified and decides. If interested, they reach out to you and quote the returned code. Metered; requires a prior get_candidate; one active offer per candidate.
get_contact Check the status of a contact request you sent: sent, accepted (they're interested — they'll reach out), or declined.
get_new_candidates Resumes new or updated since a timestamp — your standing alert. Same filters as search.
save_candidate / get_watchlist Save a candidate to your watchlist and review your saved list.
get_corpus_stats Aggregate stats about the corpus — counts, top skills, top locations.
check_subscription Your tier, status, rate limits, and usage this hour.

Search filters

search_candidates and get_new_candidates accept any combination of: skills (any-of, case-insensitive partial — "React" also matches "React 19"), location, remote_ok, seniority_levels, employment_types, work_location_types, work_authorization, open_to_relocation, experience_years_min / experience_years_max, salary_min / salary_max, availability, page, and limit.

Skills, seniority, employment type, and work-location values are normalized — plain terms and common variants resolve to the same match, so you don't have to guess exact spellings.

Rate limits

Two separate, generous limits protect candidates and reflect your plan:

  • Reveals (get_candidate) — for evaluating candidates. Capped per seat to prevent bulk harvesting.
  • Contacts (request_contact) — the metered, monetized action. Smaller per-seat caps, plus a per-candidate guard: one active offer at a time, a cooldown between offers, and blocked recruiters can't reach a candidate again.

Free accounts get a small allowance of each to try things out. Call check_subscription to see your current tier, caps, and usage.

Privacy & consent

Every profile in the corpus is explicitly opted in — never scraped. Candidates control their visibility and can delete their data anytime. The platform never exposes a candidate's name or contact to a recruiter; the candidate reveals themselves by responding. This is the contract that makes QueryQuarry a trusted place for both sides.

Frequently asked questions

What is MCP?
MCP (Model Context Protocol) is the open standard that lets AI assistants connect to external tools and data. QueryQuarry uses it to expose the talent graph so your AI can search candidates directly from your normal workflow.
How do I connect QueryQuarry to my AI?
Add QueryQuarry as an MCP connector via OAuth, or call the server with an API key. This reference walks through both so you can be querying candidates within a few minutes.
What can my AI do with QueryQuarry?
It can search anonymous candidate cards, evaluate a candidate in depth, request contact, and track outreach. Those tools let your AI source and reach out conversationally while QueryQuarry enforces consent and privacy server-side.
Does QueryQuarry give my AI a candidate's contact details?
No — the platform never returns a candidate's name or contact; it returns anonymous profiles and brokers an introduction. Identity is shared only when the candidate accepts, which keeps the corpus opt-in and free of spam.