Lusha

Lusha MCP Plugin

Find and enrich B2B contacts and companies with verified emails, direct dials, mobile numbers, and real-time buying signals from Lusha — straight from inside your AI assistant.

Supports Codex (plugins), Claude Code (Claude Code CLI / Cowork), Cursor (plugins), VS Code Copilot (GitHub Copilot Chat with MCP), and Gemini CLI (extensions).

Skills

Find and enrich

Skill	What it does
enrich-contact	Look up any person and get their verified direct and mobile phone numbers, email, and company context
prospect	Describe your ICP in plain English — get a filtered, enriched lead list with phone numbers revealed
signal-prospect	Start from a buying signal (funding, hiring surge, job change) and get the right decision makers' phones
lookalike-prospect	Give Lusha 5+ reference companies or contacts — get a matched list enriched with phone numbers

Write outreach — a two-stage pair, run in order

Skill	What it does
outreach-research	Stage 1. Capture what you sell, to whom, with what proof, and how you differentiate as a portable positioning brief (lusha-outreach-brief.md). Text only — makes no Lusha API calls
outreach-sequence	Stage 2. Turn that brief plus a contact list into short, signal-grounded Email 1 (optionally Emails 2–3 and LinkedIn messages), handoff-ready as flat CSV, with each draft's hook and personal-touch source alongside it

How it works

The find-and-enrich skills each chain multiple Lusha API calls into a complete workflow, and surface verified phone numbers prominently — direct lines and mobile numbers are first-class outputs, not an afterthought.

The outreach pair sits on top of them. outreach-research is pure conversation, producing a brief you save and reuse. outreach-sequence consumes that brief and never calls Lusha directly: it delegates signal discovery and harvest to signal-prospect, and contact enrichment to enrich-contact, so credit handling stays in one place. Every call it triggers is tagged with a reason_for_invocation starting outreach-sequence: , which is how skill-driven usage is attributed.

All clients load the same skills/*/SKILL.md files and the same Lusha MCP server — only the per-client manifest and store endpoint differ:

Client	Manifest	MCP endpoint	How to invoke
Codex	.codex-plugin/plugin.json + mcp.json	mcp.lusha.com/mcp/codex	Skills activate from natural language requests
Claude Code	.claude-plugin/plugin.json	mcp.lusha.com/mcp/claude	/enrich-contact, /prospect, etc.
Cursor	.cursor-plugin/plugin.json	mcp.lusha.com/mcp/cursor	Skills activate from natural language requests
VS Code Copilot	.github/plugin/plugin.json	mcp.lusha.com/mcp/copilot	/enrich-contact, /prospect, etc.
Gemini CLI	gemini-extension.json	mcp.lusha.com/mcp/gemini	Gemini activates the matching skill on demand

Skills reference Lusha tools by their bare logical name (e.g. contacts_search), so a single skill source works identically across all clients. Gemini CLI auto-discovers the bundled skills/ directory as extension skills.

Prerequisites

• A Lusha account with API access

Install

Codex

The Codex plugin lives at the repo root — .codex-plugin/plugin.json (manifest) and mcp.json (MCP server), with skills: "./skills/" pointing at the shared root skills/. Codex discovers it through the repo marketplace catalog at .agents/plugins/marketplace.json, which uses a url source pinned to a branch/tag. That catalog is read only by Codex/OpenAI tooling — Claude, Copilot, and Gemini keep using their own provider-specific manifests.

A url source is used instead of a local path because Codex rejects a local plugin path that resolves to the repo root (codex#17066) and silently drops symlinks during install (codex#18863). Cloning the repo over url keeps skills/ as real files at the plugin root, so no copy or symlink is needed.

Add the marketplace and install:

codex plugin marketplace add lusha-oss/lusha-mcp-plugin
codex
/plugins

Select Lusha Plugins, install the Lusha plugin, then start a new Codex thread so the skills and MCP tools are loaded. The url source installs from the ref pinned in .agents/plugins/marketplace.json, so changes take effect once they land on that ref.

Claude Code (CLI / Cowork)

/plugin marketplace add lusha-oss/lusha-mcp-plugin
/plugin install lusha

Cursor

Cursor reads the plugin manifest at .cursor-plugin/plugin.json and discovers the bundled skills/ automatically. Add the repo as a plugin marketplace, then install the Lusha plugin from .cursor-plugin/marketplace.json (catalog lusha-plugins, plugin lusha). All six skills activate from natural-language requests once the MCP server connects.

VS Code Copilot

Requires a VS Code version with agent-plugin support and the GitHub Copilot extension. The plugin bundles the MCP server and all skills together via .github/plugin/plugin.json.

1. Open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P).
2. Run Chat: Install Plugin From Source.
3. Paste the repository name: lusha-oss/lusha-mcp-plugin.

The Lusha MCP server and all six skills load automatically. Invoke a skill from Copilot Chat with /enrich-contact, /prospect, /signal-prospect, /lookalike-prospect, /outreach-research, or /outreach-sequence.

Gemini CLI

The repo ships a gemini-extension.json manifest at its root, so Gemini CLI wires up the Lusha MCP server and discovers the bundled skills automatically.

gemini extensions install https://github.com/lusha-oss/lusha-mcp-plugin

All six skills are registered as extension skills — Gemini activates the matching one on demand (e.g. when you ask it to find a contact's phone number, build a prospect list, or draft outreach). Run gemini skills list to confirm they loaded.

Authentication

The Lusha MCP server uses OAuth. The first time you invoke a Lusha skill or tool, you'll be prompted to sign in with your Lusha account. Subsequent calls reuse the authenticated session.

Skill chaining

Skills are designed to feed into each other:

• prospect → signal-prospect: build a list, then filter it to companies showing buying signals
• lookalike-prospect → signal-prospect: find lookalikes, then prioritize by signal
• enrich-contact → lookalike-prospect: enrich a single contact, then find similar people
• outreach-research → outreach-sequence: capture positioning once, then draft against it
• prospect → outreach-sequence: hand a lead list straight into drafting (optional — any CSV with full_name, company, and title works)

outreach-sequence requires a brief from outreach-research; without one it refuses to draft rather than inventing positioning. An audience is required too, but it can come from anywhere: a pasted CSV, an attached file, a single named contact, or a prospect handoff.

Interactive and automated runs

outreach-sequence runs in two modes, set out in its Execution Mode section:

• Interactive (default) — a human is in the conversation, so the skill asks about signal preferences, any audience-wide signal, and whether you want a sample before it drafts the batch.
• Automated — the caller declares a non-interactive run ("non-interactive", "headless", "automated run", or "no user is available to answer"), as an automation runner or scheduled job would. Every question resolves from a supplied value or a documented default and execution continues, because an emitted question would become the final output and the run would produce nothing.

Automated mode never relaxes the data rules: the brief and audience stay hard-required, signal and enrichment work still delegates to the other skills, and nothing may be fabricated to fill a gap.

What the drafts follow

• Email 1 is five short parts (why now, pain, what the product does, one proof point, one question), 50–90 words, with a 3–5-word subject. It asks about interest, never for a meeting, unless you ask for one.
• A signal is the reason for writing only when it is relevant to the persona's pain, dated within 90 days, and stated as the source states it: the month, never "last week"; no percentages or estimated figures. Website visits and intent topics set the timing but never appear in a message. When no signal qualifies, Email 1 leads with the persona's pain and names one real detail about the contact or their company.
• Email 2 brings one new reason (a second signal, a proof point, or a question about the pain); Email 3 closes the thread by naming Email 1's topic. Both reuse Email 1's subject so your sending tool threads them.
• LinkedIn copy is the message for once they accept; send the connection request blank unless you ask for notes (at most 200 characters).
• No placeholders. A part that needs a fact the brief lacks is left out, and one line under the table says what to add to the brief. A personal touch comes only from a Lusha enrich result that shows a prior employer: the prior title and employer, nothing more.

Output columns (0.1.3)

linkedin_message (the message for once they accept) and linkedin_connection_note replace linkedin_message_1–linkedin_message_3, and every drafted row gains hook_source and personal_touch_source. Update any Braze, Outreach or Salesloft template that used the old LinkedIn columns.

Brief format

outreach-research writes brief v2 (<!-- lusha-outreach-brief v2 -->): it adds proof points (each marked OK to name in email), titles to match per persona, an optional email style, and an (unconfirmed) marker for values from a web search that you have not confirmed. Both skills still read a v1 brief.

Contributing

Root skills/ is the single source of truth — every client (Codex, Claude, Copilot, Gemini) reads these same files, so edit skills only under skills/.

When releasing, update the ref in .agents/plugins/marketplace.json to the branch or tag Codex users should install from (e.g. master for production, or a feature branch while testing).