company-enrich-mcp
RemoteAbout company-enrich-mcp
Extracts verified emails, social links, 40+ tech stack signals, pricing tiers, and company summary
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://company-enrich-72343955726.us-central1.run.app/mcp/company-enrichclaude mcp add --transport http robertoyanguas-ops-company-enrich https://company-enrich-72343955726.us-central1.run.app/mcp/company-enrich{"mcpServers":{"robertoyanguas-ops-company-enrich":{"type":"http","url":"https://company-enrich-72343955726.us-central1.run.app/mcp/company-enrich"}}}Open in Cursor{"servers":{"robertoyanguas-ops-company-enrich":{"type":"http","url":"https://company-enrich-72343955726.us-central1.run.app/mcp/company-enrich"}}}Open in VS Code{"mcpServers":{"robertoyanguas-ops-company-enrich":{"type":"http","url":"https://company-enrich-72343955726.us-central1.run.app/mcp/company-enrich"}}}{"mcpServers":{"robertoyanguas-ops-company-enrich":{"serverUrl":"https://company-enrich-72343955726.us-central1.run.app/mcp/company-enrich"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
2| Parameter | Type | Required | Description |
|---|---|---|---|
| domain | string | Yes | Company domain name or URL (e.g. stripe.com, linear.app) to enrich. |
| tx_hash | string | No | Use only after paying on-chain: the Base (0x...) or Solana USDC transfer hash. It is verified on-chain before the call runs, and any overpayment is returned as an agd_pass_ credit key. |
| agent_id | string | No | Your agent ID, or the agd_pass_ key you got from a payment. It tracks your free calls and credit, so treat it like a password. Omit it to use an ID derived from your IP and client. |
Details
The maintainer has not added details yet. Check the README in the repository.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.