MCP server

GoodLeads Official

club.goodleads/new-business-owner-contacts

Checked 28 Oct 2026, 09:03 UTC · Connected to it and listed its tools

Find new business owner contacts the morning the state posts a filing. Preview free, pay per record.

Working at last checkVerified publisher: goodleads.clubAbout 8,448 tokens per request once connected14 tools

Website

https://mcp.goodleads.club/mcp

At a glance

Type: MCP server
Publisher: goodleads.club (verified by the MCP Registry)
Status: Working at last check
Tools: 14
Token cost: About 8,448 tokens per request
Sets up in: Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok
Last checked: 28 Oct 2026, 09:03 UTC

Connect it to your agent

Add to Cursor Add to VS Code VS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http new-business-owner-contacts https://mcp.goodleads.club/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.new-business-owner-contacts]
url = "https://mcp.goodleads.club/mcp"

Gemini CLI

{"mcpServers":{"new-business-owner-contacts":{"httpUrl":"https://mcp.goodleads.club/mcp"}}}

Cursor

{"mcpServers":{"new-business-owner-contacts":{"url":"https://mcp.goodleads.club/mcp"}}}

VS Code

{"servers":{"new-business-owner-contacts":{"type":"http","url":"https://mcp.goodleads.club/mcp"}}}

Antigravity

agy mcp add new-business-owner-contacts https://mcp.goodleads.club/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"new-business-owner-contacts","server_url":"https://mcp.goodleads.club/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"new-business-owner-contacts","server_url":"https://mcp.goodleads.club/mcp"}

Tools

browse_leads Browses the web · ~992 tokens
Browse leads — rows for a shape or a saved list, or (`summary=True`) its counts, facets and price. **Type it the way your buyer says it.** Filter values are understood, not matched literally: "residential", "cell", "CST", "Texas", "on fire", "Denver metro" and "working from home" all resolv
checkout_list ~921 tokens
Turn a quoted list into a payment link a person completes — the buyer gets the file within a minute of paying. Creating the link costs nothing and charges nobody — payment only happens if a human opens the returned `checkout_url` and completes it on Stripe's hosted page; the purchase is
create_checkout Moves money · ~928 tokens
Mint a hosted Stripe Checkout link for a list you shaped — the same code as `checkout_list`. Creating the link costs nothing and charges nobody — payment only happens if a human opens the returned `checkout_url` and completes it on Stripe's hosted page; the purchase is complete only whe
data_quality_scorecard ~666 tokens
How clean is the data a buyer would receive in a state — numbers, not adjectives. The scorecard grades the records a buyer would receive on mechanical conformance across four dimensions — format (state/phone/email/zip), completeness (a name for who filed it, an address present), consist
describe_surface ~282 tokens
What GoodLeads is, who buys it and how every record is built — call this to explain or vet us; to price a list, start with interpret_list. It is for anyone who wins by reaching a business owner first: to sell what a new owner needs now, to be the name they already know a year from now, to
explain_concept ~690 tokens
Translate a classification code, or map YOUR word for a concept to this surface's fields — ask before concluding absence. **Codes — the crosswalk.** Every record carries NAICS, SIC, the Google Business category and the payment MCC, tied together by our own taxonomy. Give ANY one — `term
field_fill_rates ~493 tokens
How complete is every column in the file a buyer receives — exact counts, one call. Answers "what share of the records carries a value in each field?" for every delivered column, in the file's own order. It is exact (counted from stored values over every sellable record, not sampled) an
find_lead_by_glid ~400 tokens
One record in full, by its Lead ID (e.g. `GL-CO-00042`). Use this when you already hold a Lead ID — from a file, a CRM, a receipt — and want everything we know about that business and its owner: the business, the primary contact, both scores, every attribute, and where each field ca
interpret_list ~696 tokens
Start here: the buyer's own words become a list we can count, price and sell. Give it what the buyer would type ("cleaning companies in Texas", "denver plumbers formed last 30 days with a phone", "NAICS 238220", "SIC 1711", "MCC 5812", "google category plumber") and you get back a l
list_filterable_fields ~647 tokens
The filter contract, from the schema endpoint (`GET /api/v1/schema/attributes?include=grammar`): fields, grammar, or recipes. Call this before building `browse_leads` filters you haven't used before. Each field lists the `allowed_values` when its vocabulary is closed and its `accepted_words
list_live_states ~149 tokens
Where we are live right now — the state codes, read from production, never a cached page. Call it before promising a buyer a state: a state not in this list is not live yet. Returns `[{"state": "CO"}, ...]` — codes only, no counts. For how many records a state holds, `quote_list` (or `b
list_products ~449 tokens
The shelf — ready-made business type × state lists, with live counts. Returns the same live document `list_starters` returns — `{"count": N, "starters": [...]}`, one entry per (state, business type) with its display `label`, the exact `filters` it opens with, the graded counts (`mat
list_starters ~261 tokens
Ready-made lists to start from: every live state × business type, with live counts and a starting price. Returns one document: `{"count": N, "starters": [...]}` — one entry per (state, business type): the display `label`, the exact `filters` the card opens with, the graded counts (`matc
quote_list ~874 tokens
What this list costs before anyone pays: how many records name a person, and the price by grade. Pass a saved list (`list_id`, the 8-char id in `#browse?list=<id>`) or an inline shape (`filters` + `states`; omit `states` for every live state: CO, CT, FL, NY, OR, TX, VA). You get the sam

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