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 returns403. 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.
Skill File (System Prompts)
Add the OutX skill file to your project for static API context: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 tohttps://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

