List building has always been a filter-fiddling exercise: stack dropdowns, eyeball the count, adjust, export, discover the list wasn't what you meant, repeat. An AI agent connected to the database turns that loop into a conversation — you describe the audience, the agent interrogates the data and refines until the numbers make sense, and only then do you spend money.
This post walks through doing exactly that with Claude and Argorant over MCP, the Model Context Protocol. The same setup works for any MCP-capable agent; Claude is just the one most people start with.
The two-minute setup
Argorant exposes a remote MCP server at https://mcp.argorant.com/mcp. In Claude's connector settings (or via `claude mcp add` in Claude Code), add that URL as a remote server. The first call triggers an OAuth flow in your browser: you sign in to your Argorant account, approve the connection, and the agent receives a scoped token. No API keys pasted into config files, and you can revoke the connection from your account at any time.
Once connected, Claude sees 13 tools covering the full workflow: searching companies and people, counting matches, previewing records, revealing contacts, and exporting lists. MCP access is included on every paid plan — it isn't an enterprise add-on, and plans start under $100/month.
The economics: free to look, metered to take
The tool design follows one rule that makes agent use safe: reading shapes is free, extracting contacts is metered. Counts cost nothing. Previews — company names, titles, locations, everything except the actual contact details — cost nothing. The agent can run twenty exploratory queries while narrowing a definition and your credit balance doesn't move.
Credits are only spent on reveals and exports, when verified contact data actually leaves the system. And because Argorant verifies via live SMTP at export and charges 0 credits for invalid results, the metered step only bills you for addresses that pass. An agent can't burn your balance on exploration, and it can't burn it on bounces either.
A real session, end to end
Here's what the loop looks like in practice. You ask: "Find heads of operations at mid-size logistics companies in Germany and the Netherlands." Claude calls the count tool with its first interpretation of that request — maybe 50–500 employees, a transportation-and-logistics industry filter, two country codes — and reports something like 4,800 matching people.
Then the interesting part: you interrogate the result before paying for it. "How does that split by country?" "Show me 10 sample titles so I can see what 'head of operations' is matching." Claude runs preview calls, you spot that the title filter is catching warehouse shift leads, you tighten it, the count drops to 2,100. Two more refinement rounds get you to a definition you actually believe.
Only then do you say "reveal the top 200 and export them as CSV." That's the first moment credits are spent — on a list you've already inspected from three angles, with every address SMTP-verified on the way out.
A practical tip for repeatability: once a definition is dialed in, ask Claude to restate the final filter set explicitly — industries, headcount bands, countries, title patterns. Paste that into a project file or system prompt and the next session starts from a known-good baseline instead of re-deriving the ICP from scratch.
If you'd rather stay in the terminal
The same workflow is available without an agent in the middle. The Argorant CLI is published on npm — `npx argorant` runs it with zero install — and exposes search, counts, reveals, and exports as commands you can script. It authenticates against the same account and the same credit balance, so a cron job, a CI step, or a quick one-off pull from a shell all draw from one place.
The CLI is also the pragmatic debugging layer for agent workflows: when you want to confirm exactly what a filter matches before encoding it into an agent's instructions, one command gives you ground truth.
Why this beats the dashboard loop
The structural win is that iteration becomes nearly free, in both time and credits. In a traditional tool, every refinement costs a page reload and every mistake costs an export. In the agent loop, refinement is a sentence and mistakes are caught at the preview stage, before money moves.
It also means list building composes with everything else your agent does. Claude can pull the list, draft the first-touch copy referencing each company's industry, and hand the file to your sequencer — one conversation, with 614M contacts across 184 countries sitting behind a protocol the agent already speaks. Connect once, then stop clicking dropdowns.