Your agent, wired into the graph.
150M people. 10M companies, embedded. Live buying signals. gtm.dev exposes the graph as typed tools an agent calls directly — resolve, search semantically, verify — and every point behind this page is a real company in vector space.
27 tools · one key · usage-priced · ✓ verified, or null — never a guess
Three planes of data. One graph underneath.
Not a reselled database — a graph we scrape, resolve, enrich, and verify ourselves. Every plane joins on the same canonical org id, so your agent never joins on a company name again.
Org charts, not contact lists. 48M people mapped into reporting structures — role level, teams, tenure — inside 150M+ professional profiles with titles scored for confidence. Ask for “VP+ in finance” and mean it.
org charts · professional profiles · title confidenceFirmographics plus vectors. Industry, size, geo, description — and 8M vector embeddings over the graph, so “API-first payroll for global teams” is a query, not a filter puzzle.
firmographics · 8M embeddings · lookalikesThe “why now” plane. 3M ads ranked by spend, 1.8M job posts, 3.5M funding histories, 4.4M tech detections, de-anonymized site visitors — each resolved to a company you can act on.
ads · hiring · funding · tech · intentSearch by meaning, not by filter.
Firmographic filters can’t say “feels like Stripe, but for logistics” — there’s no dropdown for that. Embeddings can. The whole company graph lives in vector space — the atlas you scrolled past is it — so your agent describes the ICP in plain language and gets a ranked neighborhood back.
Lookalikes fall out for free: seed one great customer, get its nearest neighbors by cosine — the expansion list every agent asks for eventually.
concept_searchnatural-language ICP → semantically ranked companieslookalikesseed company → nearest neighbors, scoredsize_tamthe same query → a count + a reusable filter setWho’s spending. Who’s hiring. Who just raised.
A contact database tells you who exists. Signals tell you why now — active budget, active pain, active change — every one resolved to a real company.
› who_is_advertising({ keyword: "crm", country: "IN", platform: "meta+linkedin" }) // ranked by aggregated impressions — a spend floor, not a guess
Active budget is the loudest signal there is.
3M ads across Meta and LinkedIn, resolved to real companies and pre-aggregated into a spend-ranked index of 152,384 advertisers. If they're paying to reach a market, they have budget, urgency, and a GTM motion — this quarter, not last year.
public ad libraries · meta + linkedin · spend-ranked, refreshed continuouslySeven calls. Zero glue code.
Every GTM agent runs the same loop: find the trigger, resolve the account, qualify it, find the person, verify the channel, personalize. Today that loop crosses five vendors and a pile of glue code.
On gtm.dev it’s one server — every hop typed, every record carrying provenance. The trace on the right is the whole integration.
Verified, or nothing.
We don’t resell a database. We operate the pipelines and expose the output — which means we can tell you where every field came from, and we’d rather return null than guess.
We run the pipelines
The graph is built in-house, end to end. No vendor’s stale dump underneath, no license that vanishes. When a field is wrong, we can trace it to the run that produced it.
Emails verified at call time
A waterfall of finding providers, then live verification — status and score on every address. find_email does the work when you call it, not in a batch last quarter.
Messy input, canonical output
“Acme Labs”, acme.io, half a URL — the resolver’s trigram + description + LLM cascade lands on one canonical entity with a confidence score. Every other tool composes on it.
find_email → nullWe checked five providers and verified none — so you get nothing, not a firstname.lastname guess that bounces and burns your sender. Honesty is cheaper than a blocked domain.
Wired for agents, not dashboards.
Designed as tools an LLM calls — not an API bolted onto a UI product. Connect the MCP server, or hit the same tools over REST.
JSON Schema on every input; structured, versioned outputs. Your agent never parses prose.
Ambiguous input returns candidates with confidence — the resolution step agents otherwise get wrong.
gtm_live_sk_… / gtm_test_sk_… — test mode is free and fake-data-safe.
{
"mcpServers": {
"gtm": {
"url": "https://mcp.gtm.dev",
"headers": { "authorization": "Bearer gtm_live_sk_…" }
}
}
}
// that's the whole integration — 27 tools appear in your agentEngineers building GTM agents
Founders and platform teams wiring an LLM to “find the right people at the right accounts.” You want primitives, not another SaaS login.
who_is_advertising → find_people → find_email
The Clay-and-n8n crowd
You already orchestrate workflows. Point them at MCP tools with verified data behind them instead of six enrichment columns that disagree.
build_list → score_accounts → enrich_person
B2B data inside your product
A vertical CRM, an analytics tool, a sales copilot — embed enrichment and signals via API without becoming a data company yourself.
resolve_company → enrich_company → company_tech
RocketSDRThe flagship reference implementation: a full AI-SDR product — discovery, research, outreach — running entirely on these tools. If gtm.dev can power that, it can power yours.
RocketSDR — built on gtm.devPriced like infrastructure.
Usage-based credits, metered per call. Resolution is nearly free because you’ll call it constantly; verification costs more because it does more. No seats, no platform fee.
Test mode is free forever. 1,000 live credits to start — no card, no call with sales.
| tool class | examples | per call |
|---|---|---|
| resolve | resolve_company · resolve_geo | 0.1 credit |
| search & enrich | concept_search · find_people | 1 credit |
| signals | who_is_advertising · find_funded | 1 credit |
| find + verify | find_email · find_phone | 2 credits |
| research | research_company · research_contact | 5 credits |
Stop scraping.
Start calling tools.
Connect the MCP server and your agent has the graph in the next five minutes — 150M people, 10M companies embedded, and the signals that say why now.