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Hunter vs People Data Labs

Hunter scores higher on the AgentReady, 57/100 against 50/100. They differ on 11 of the 41 signals. Which ones decides whether an agent can adopt them without a person watching.

What each one is

Hunter. Hunter is an all-in-one email outreach platform for finding and connecting with professional contacts.

People Data Labs. Talent sourcing platform that builds workforce data, so you don't have to.

Where Hunter is ahead

Hunter passes clear canonical domain and mcp discoverable, and People Data Labs does not. That is discover, whether an agent can find the product at all without being told it exists.

It also holds understand: authentication documented. People Data Labs misses it.

And on adopt, agent-compatible signup flow and mcp integration available. People Data Labs misses those.

Finally, on operate, agent compatibility verified. People Data Labs misses it.

Where People Data Labs is ahead

People Data Labs passes clear product positioning and public docs discoverable, and Hunter does not. That is discover, whether an agent can find the product at all without being told it exists.

It also holds adopt: fast time to first request and copyable quickstart. Hunter misses those.

And on operate, structured, predictable output. Hunter misses it.

What neither does

Both fail structured api reference, openapi / spec quality, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented, no mandatory sales call, programmatic credential creation, cli available, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable. If your agent needs any of those, you will be building it yourself either way.

Score, pillar by pillar

The AgentReady splits into four pillars, scored separately, because a product can be easy to find and still impossible to adopt.

Discover. People Data Labs leads 80 to 73. Hunter misses clear product positioning, public docs discoverable; People Data Labs misses clear canonical domain, mcp discoverable.

Understand. Hunter leads 31 to 23. Hunter misses structured api reference, openapi / spec quality, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented; People Data Labs misses structured api reference, openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented.

Adopt is whether an agent can get a key and make its first successful call without a human in the loop. Hunter leads 67 to 50. Hunter misses no mandatory sales call, programmatic credential creation, fast time to first request, copyable quickstart, cli available; People Data Labs misses no mandatory sales call, agent-compatible signup flow, programmatic credential creation, cli available, mcp integration available.

Operate. Hunter leads 56 to 47. Hunter misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable; People Data Labs misses machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, agent compatibility verified.

Pricing

Hunter does not publish a machine-readable starting price and has a free tier. People Data Labs does not publish one and has a free tier.

Signal by signal

Which to pick

Hunter clears more of the signals an agent needs, so it is the safer default for unattended use. Full profiles: Hunter and People Data Labs. Alternatives to each: Hunter, People Data Labs.

An agent can fetch this as data: POST /v1/compare {"slugs": ["hunter", "peopledatalabs"]}