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Two things must be true before any call works

1. A person on your team runs the OutX Chrome extension in their own Chrome, and it was active in the last 48 hours. OutX collects LinkedIn data through that person’s logged-in browser session. Without a live extension, every API call returns 403. There is no headless mode and no server-side fallback. Install it from the Chrome Web Store, sign into LinkedIn in that browser, and keep the browser open. See Chrome Extension. 2. You have an API key, sent as the x-api-key header on every request. Get it at mentions.outx.ai/api-doc, or programmatically from the OTP endpoints (send, verify), which need no browser but do deliver the 6-digit code by email, so an unattended agent needs read access to that inbox or a person to relay it. Store it as an environment variable: export OUTX_API_KEY="your-key". Pages under outx.ai/docs are the only current documentation. When a product guide and the API reference disagree, the API reference is right. You pace the LinkedIn actions yourself. There is no request rate limit on the API and no endpoint returns 429. LinkedIn actions must still be paced: roughly 100 reads, 50 likes, 25 comments, 50 messages, 20 connection requests a day per LinkedIn account, spaced out, never in a burst; see Recommended pace. OutX is built for AI agent integration. Use these resources to connect your AI workflows to LinkedIn data and social listening.
Load How OutX works as context before your agent calls anything. One page: the watchlist pipeline, prompt mode vs keywords mode, what a prompt update regenerates, retry keys, and how to handle a refused prompt.

Docs for Agents

Four ways to connect OutX to your AI agent:

MCP Server

Connect Claude, Cursor, or any MCP-compatible agent to OutX. 33 tools covering the full API, Reddit watchlists included. Best for interactive agents.

Skill File

Structured API reference with guardrails, parameter tables, and code examples. Best for system prompts.

llms.txt

Concise index of all documentation pages with one-line descriptions. Best for discovery.

Full Docs

Every API page in one file: API reference, LinkedIn Data, integrations, resources. The product UI guide is separate, in llms-guide.md.

Quick Start Prompts

Copy these prompts into your AI agent to get started:

Monitor LinkedIn for keywords

Fetch a LinkedIn profile

AI Builder Integrations

MCP (Claude Desktop / Cursor / Claude Code)

Install the OutX MCP server for full API access via natural language. It registers 33 tools: list_watchlists plus 16 for the four watchlist types (keyword, Reddit, people, company), get_posts, get_interactions, like_watchlist_post, comment_on_watchlist_post, get_team, and 11 LinkedIn Data tools.
See MCP Server setup guide for the full tool list and configuration details.

Skill File (System Prompts)

Add the OutX skill file to your project for static API context:
Reference it in your CLAUDE.md or Cursor rules to give the AI agent full API context.

Any LLM / Agent Framework

Include the skill file content in your system prompt, or point your agent to https://outx.ai/docs/llms.txt for documentation discovery. See also the LangChain and Python SDK integrations.

What’s Next

Watchlist Quick Start

Create watchlists and retrieve posts via API

LinkedIn Data Quick Start

Fetch LinkedIn profiles in 2 minutes

API Reference

Full API documentation

MCP Server

Connect AI agents to OutX via MCP