Waterfall lead enrichment for AI agents. Cascades through Apollo, Clearbit, and Hunter to build the most complete lead profile in a single call.
Waterfall lead enrichment for AI agents. Cascades through Apollo, Clearbit, and Hunter to build the most complete lead profile in a single call. Exposed over MCP by the leadenrich mcp mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
Everything the assistant can do here goes through one of these:
enrich_lead — Full waterfall enrichment for a single lead (email, domain, or name+domain)find_email — Discover an email from first name + last name + company domainenrich_company — Company firmographic data by domain (industry, size, revenue, etc.)enrich_batch — Batch enrich up to 25 leads concurrentlycheck_usage — Quota, cost tracking, and remaining lookupshealth_check — Server status, configured providers, and cache statsSmithery — This repo includes smithery.yaml for Smithery deploymentYou will need 4 environment variables: APOLLO_API_KEY, CLEARBIT_API_KEY, HUNTER_API_KEY, LEADENRICH_API_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
pipInstallation goes through your MCP client rather than a global install: point it at leadenrich-mcp on PyPI and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Among the team communication options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Leadenrich MCP's toolset — enrich_lead, find_email, enrich_company and 4 more — is a fair guide to whether it matches your workflow. It is maintained by carsonlabs; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Leadenrich MCP's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| enrich_lead | Full waterfall enrichment for a single lead (email, domain, or name+domain) |
| find_email | Discover an email from first name + last name + company domain |
| enrich_company | Company firmographic data by domain (industry, size, revenue, etc.) |
| enrich_batch | Batch enrich up to 25 leads concurrently |
| check_usage | Quota, cost tracking, and remaining lookups |
| health_check | Server status, configured providers, and cache stats |
| Smithery | This repo includes smithery.yaml for [Smithery](https://smithery.ai/) deployment. |
{
"mcpServers": {
"leadenrich": {
"command": "uvx",
"args": ["leadenrich-mcp"],
"env": {
"APOLLO_API_KEY": "your-value",
"CLEARBIT_API_KEY": "your-value",
"HUNTER_API_KEY": "your-value",
"LEADENRICH_API_KEY": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
pip| Variable | Description | Required |
|---|---|---|
| APOLLO_API_KEY | Credential the server authenticates with. | Yes |
| CLEARBIT_API_KEY | Credential the server authenticates with. | Yes |
| HUNTER_API_KEY | Credential the server authenticates with. | Yes |
| LEADENRICH_API_KEY | Credential the server authenticates with. | Yes |
Your assistant inside the workspace — read channels, search history, post messages and tame the noise.
Inbox intelligence — search, read, draft and send Gmail through your assistant with OAuth auto-setup.
Read and write Jira, Confluence, Bitbucket, JSM and Compass from your AI client — with your own permissions.
Read and send Telegram messages through your assistant — chats, channels and history via the client API.
Enables Discord bot integration with Model Context Protocol (MCP) compatible applications like Claude Desktop.
Exposes REST APIs defined by OpenAPI specifications as Model Context Protocol (MCP) tools, facilitating seamless integration into MCP-based workflows.