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, 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 personalized, signal-grounded Email 1 (optionally E2/E3 and LinkedIn), handoff-ready as flat CSV |
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.
- Open the Command Palette (
Cmd+Shift+P / Ctrl+Shift+P).
- Run Chat: Install Plugin From Source.
- 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 signalslookalike-prospect → signal-prospect: find lookalikes, then prioritize by signalenrich-contact → lookalike-prospect: enrich a single contact, then find similar peopleoutreach-research → outreach-sequence: capture positioning once, then draft against itprospect → 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.
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).