Most GTM tooling was built for a human to click through a dashboard. That model breaks down the moment your buyer signal lives somewhere a dashboard can’t watch continuously — and for a huge slice of the developer-tool market, the earliest and most honest buying signal is GitHub activity: someone starring a competitor’s repo, opening an issue against your category, or forking a project to evaluate it.

An AI agent doesn’t need a dashboard to act on that. It needs tool calls it can chain together, and a data layer that returns structured, decision-ready output at every step. That’s the shape of the pipeline DataForGTM exposes to agents: four stages, four categories of tool, one MCP endpoint.

The four-stage pipeline

Every account that turns into a booked meeting passes through the same sequence:

• Stage 1 — Detect. developer_intent and company_intent scan public GitHub activity — stars, forks, issue opens, PR submissions against a target technology — and surface who’s evaluating your category right now. This stage is anonymous by design and free to call: no PII, just signal volume, so an agent can run it constantly without burning budget.
• Stage 2 — Qualify. score_against_icp and company_intel take the accounts surfaced in stage 1 and filter for fit. score_against_icp returns a numeric match score plus the specific signals that drove it — not a black-box label the agent has to trust blindly. company_intel adds firmographics and active tech stack pulled from public org activity.
• Stage 3 — Identify. This is where a specific GitHub username becomes a specific person your agent can actually reach. deanonymize resolves a username to a LinkedIn identity through multi-stage validation — career trajectory, professional context, and industry signals all have to agree before a match is returned, which is why the tool holds 90%+ precision instead of just guessing the first same-name profile. dossier goes deeper, returning influence tier, activity cadence, and tech stack for a single developer.
• Stage 4 — Engage. The agent now holds a qualified, identified contact with the context that got them there. That’s a fundamentally different lead than a name pulled from a purchased list — this person was already evaluating your category before your agent ever reached out.

Why this matters for agent-first workflows

The point of separating detect and qualify from identify is cost discipline. Stages 1 and 2 are free and unlimited, so an agent can sweep hundreds of repos and companies looking for movement. Stage 3 — the part that costs credits — only runs against accounts that already cleared the ICP bar. You’re not paying to identify people who were never going to be a fit in the first place, and a miss on deanonymize costs nothing: you’re charged only when a match is actually returned.

That pricing shape mirrors how an agent should reason about the funnel anyway — cheap, wide detection at the top, and spend concentrated at the point closest to the outcome you actually want.

A sample call

Here’s what stage 1 looks like as a real request, the same shape an agent would emit after deciding to check a competitor’s repo for evaluation activity:

curl -X POST https://dataforgtm.ai/api/v1/developer-intent \
  -H "Authorization: Bearer $DATAFORGTM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"repo":"qdrant/qdrant","limit":25}'

The response is a ranked list of developers and companies showing activity against that repo — the raw material stage 2 filters down and stage 3 resolves to real identities.

No dashboard, because there’s no queue to check

The reason this is worth building as tool calls instead of a UI isn’t novelty — it’s that an agent working a pipeline doesn’t want to wait for a human to log in and review a queue. developer_intent and company_intent are cheap enough to poll on a schedule; score_against_icp and deanonymize are precise enough that the agent can act on the output directly, draft outreach, and hand off a contact with the reasoning already attached. Every step returns structured JSON an agent can chain into the next call, whether it’s calling from an MCP client like Claude or Cursor, or hitting the OpenAPI endpoints directly from a Clay or n8n workflow.

The underlying claim is simple: the best leads for a developer-tool company are the ones already showing intent in public. The job of the pipeline is just to notice, qualify, and resolve them faster than a human would — and hand off a list where every contact earned their place on it.