Linkedin Posts

io.github.johnisanerd由 GitHub 帳號 johnisanerd 發布v1.0.0更新於 Oct 26, 2026

Scrape and analyze public LinkedIn posts as structured JSON via the Apify LinkedIn Posts API.

發布方自己的服務:posts.design

已驗證Streamable HTTP可網頁執行網頁搜尋與抓取溝通與協作資料與分析

概覽

AI 產生的概覽

由 Apify 託管的 MCP 伺服器,可將公開的 LinkedIn 貼文擷取為結構化 JSON,包含內文、互動數據與作者資訊。

可以做什麼
  • 依個人檔案網址探索該使用者近期發布的公開貼文,可限制數量並依日期篩選
  • 依貼文網址直接擷取指定貼文,每次最多 1000 則
  • 每則貼文回傳一列格式一致的 JSON,含內文、主題標籤、媒體、連結、貼文類型與日期
  • 提供按讚、留言、分享數,以及作者姓名、頭銜、追蹤者數與部分熱門留言
  • 每列附有淺白的 summary 欄位,沒有公開貼文的檔案會回傳錯誤列而不中斷整批作業
適用情境
適合對公開 LinkedIn 內容進行內容研究、社群聆聽與互動數據分析,或讓助理把某人的近期貼文直接整理進試算表、資料庫或 BI 工具,而不必自行建置蒐集機制。
執行需求
需要 mcp.apify.com 上的遠端 MCP 端點並透過 MCP 用戶端連線;需要 Apify 帳號,可用 OAuth 登入或以 Bearer Authorization 標頭提供 Apify API 權杖。README 中的本機範例另需 Python 3.11 以上,並設定 APIFY_API_TOKEN 環境變數。
安裝前請注意
依回傳的貼文數量計費,大量執行會產生費用,建議限制每個檔案的貼文數以控制支出。此服務只讀取公開的 LinkedIn 頁面,不索取 LinkedIn 帳號或工作階段 Cookie,但仍需要你的 Apify 帳號或權杖,且請求是在 Apify 的伺服器上執行而非本機。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Linkedin Posts,將其新增到工作區。
  2. 連接 所需的 憑證。它們會被私密儲存,絕不會在公開頁面上展示。
  3. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/io.github.johnisanerd%2Flinkedin-posts/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。 客戶端會在首次連接時完成 OAuth 登入。

{
  "mcpServers": {
    "linkedin-posts": {
      "type": "http",
      "url": "https://mcp.apify.com/?tools=johnvc/linkedin-posts-api"
    }
  }
}

README

📝 LinkedIn Posts API: Posts & Engagement to Structured JSON

The most efficient, reliable, and developer-friendly way to use the LinkedIn Posts API.

Actor page: apify.com/johnvc/linkedin-posts-api Input schema: apify.com/johnvc/linkedin-posts-api/input-schema

Give it a public LinkedIn profile URL and it discovers that person's recent posts, or pass specific post URLs to fetch directly. You get back one clean JSON row per post: text, reactions, comments, shares, hashtags, media, and author details. It is built API-first and MCP-ready, so you can call it from Python or drive it as a tool from an AI agent.

Video Walkthrough

[Watch the walkthrough]

Quick Start

Prerequisites

  1. Clone the repository

    bash
    git clone https://github.com/johnisanerd/Apify-LinkedIn-Posts-API.gitcd Apify-LinkedIn-Posts-API
  2. Install dependencies with UV

    bash
    # Install UV if you do not have it:curl -LsSf https://astral.sh/uv/install.sh | sh
    # Install project dependencies:uv sync
  3. Configure your API key

    bash
    cp .env.example .env# Edit .env and add your Apify API key# Get your free API key at: https://apify.com?fpr=9n7kx3
  4. Run the example

    bash
    uv run python linkedin-posts-api-example.py

Alternative: set the API key directly

bash
export APIFY_API_TOKEN="your_api_key_here"uv run python linkedin-posts-api-example.py

Why Use This LinkedIn Posts API?

A URL in, structured data out. You never touch collection infrastructure. Pass a profile URL (or specific post URLs) and get flat, predictable fields you can load straight into a sheet, a database, or a BI tool.

Two ways to collect. Discover a profile's recent posts (newest first, capped and optionally date-filtered), or fetch a known set of posts by URL, up to 1000 per run.

Pay per post. Billing is per post returned, with no per-run setup fee, so you only pay for what is delivered.

Reliable and predictable. Every post comes back with the same field shape, and a profile with no public posts returns a clear error row instead of failing the whole run.

MCP-ready. Call it as a tool from Claude, Cursor, and other AI agents (see the install sections below).

Features

Core Capabilities

  • Discover a profile's recent posts by profile URL, or fetch specific posts by URL
  • Post text, hashtags, media, and links, plus post type and date
  • Reactions, comments, and shares on every post
  • Author name, headline, follower count, and a sample of top comments

Data Quality

  • One consistent JSON row per post, every time
  • A plain-language summary field on every row for quick scanning and AI use
  • A clear error row for a profile with no public posts, so one empty profile never sinks the batch

Usage Examples

Discover a profile's posts

json
{  "profileUrls": ["https://www.linkedin.com/in/williamhgates"],  "maxPostsPerProfile": 5}

Limit discovery to a date range

json
{  "profileUrls": ["https://www.linkedin.com/in/williamhgates"],  "maxPostsPerProfile": 50,  "startDate": "2025-01-01",  "endDate": "2025-12-31"}

Fetch specific posts by URL

json
{  "postUrls": [    "https://www.linkedin.com/posts/williamhgates_activity-7446904645010210816"  ]}

Input Parameters

ParameterTypeRequiredDefaultDescription
profileUrlslist[str]one of these-Public LinkedIn /in/ profile URLs to discover posts from. Up to 25 per run.
postUrlslist[str]one of these-Specific LinkedIn post URLs to fetch directly. Up to 1000 per run.
maxPostsPerProfileintNo20Max posts per profile in discover mode (max 200). Caps cost. Ignored for post URLs.
startDatestrNo-Only discover posts on or after this date (YYYY-MM-DD). Discover mode only.
endDatestrNo-Only discover posts on or before this date (YYYY-MM-DD). Discover mode only.

Supply at least one of profileUrls or postUrls.

Output Format

Each post is returned as one JSON row:

json
{  "result_type": "post",  "postId": "7446904645010210816",  "postUrl": "https://www.linkedin.com/posts/williamhgates_activity-7446904645010210816",  "postType": "post",  "datePosted": "2025-06-01T12:00:00.000Z",  "text": "A few books shaped how I think about clean energy this year...",  "hashtags": ["cleanenergy", "books"],  "authorName": "williamhgates",  "authorHeadline": "Co-chair, Bill & Melinda Gates Foundation",  "authorUrl": "https://www.linkedin.com/in/williamhgates",  "authorFollowers": 37000000,  "numLikes": 12045,  "numComments": 843,  "numShares": 210,  "summary": "Post by williamhgates, 12,045 reactions, 843 comments, posted 2025-06-01"}

The numShares field is returned when the post has shares.


FAQ

Will this put my LinkedIn or LinkedIn Recruiter account at risk?

No. It reads public LinkedIn pages only, the ones anyone can open while signed out. It never logs in and never asks for your cookies: there is no input for an account, a password or an li_at session cookie. Nothing it does is tied to your LinkedIn account, your LinkedIn Recruiter seat or your Sales Navigator license. The requests run on Apify's servers, not in your browser, so nothing connects them to you, and you can keep working in Recruiter while a run is going.


Install in Claude Cowork Desktop

[Install in Claude Cowork Desktop]

Cowork is the desktop app's automation mode. To give it the LinkedIn Posts API as a tool, add the Apify MCP server as a connector.

  1. Open the Claude desktop app and go to Settings → Connectors (or Settings → Developer → Edit Config to edit claude_desktop_config.json directly).
    • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows: %APPDATA%\Claude\claude_desktop_config.json
  2. Add the Apify MCP server, preloaded with only this Actor:
json
{  "mcpServers": {    "apify": {      "command": "npx",      "args": [        "-y",        "mcp-remote",        "https://mcp.apify.com/?tools=actors,docs,johnvc/linkedin-posts-api"      ]    }  }}
  1. Restart the app. When Cowork first calls the tool, complete the OAuth prompt in your browser, or add your Apify API token in the connector settings to skip OAuth.
  2. In a Cowork chat, confirm the tool is available and ask it to run the LinkedIn Posts API.

Download the desktop app and start a free trial: https://claude.ai/referral/uIlpa7nPLg More help: https://docs.apify.com/platform/integrations/claude-desktop


Install in Claude Code

[Install in Claude Code]

Claude Code is the command-line tool. Add the Actor's MCP server with one command:

bash
claude mcp add --transport http apify \  "https://mcp.apify.com/?tools=actors,docs,johnvc/linkedin-posts-api"

To use a token instead of browser OAuth:

bash
claude mcp add --transport http apify \  "https://mcp.apify.com/?tools=actors,docs,johnvc/linkedin-posts-api" \  --header "Authorization: Bearer YOUR_APIFY_TOKEN"

Then verify with claude mcp list, or run /mcp inside a session. Ask Claude Code to call the LinkedIn Posts API.

Try Claude Code free: https://claude.ai/referral/uIlpa7nPLg Claude Code MCP docs: https://code.claude.com/docs/en/mcp


Install in Claude (website)

[Install in Claude (website)]

On claude.ai you add Apify as a connector, then enable just this Actor's tool.

  1. Go to Settings → Connectors → Browse connectors and search for Apify MCP server. Install it (enable or update if prompted).
  2. When connecting, authenticate with your Apify API token, and enable the tool johnvc/linkedin-posts-api.
  3. In any chat, open + → Connectors and turn on Apify.
  4. Alternatively, choose Add custom connector and paste the full MCP URL https://mcp.apify.com/?tools=actors,docs,johnvc/linkedin-posts-api, using OAuth when prompted.
  5. Ask Claude to run the LinkedIn Posts API.

Open Claude on the web: https://claude.ai/referral/uIlpa7nPLg


Install in Cursor

[Install in Cursor]

Cursor reads MCP servers from a project file at .cursor/mcp.json.

  1. In your project, create .cursor/mcp.json:
json
{  "mcpServers": {    "apify": {      "url": "https://mcp.apify.com/?tools=actors,docs,johnvc/linkedin-posts-api"    }  }}
  1. If you prefer token auth over browser OAuth, add a header:
json
{  "mcpServers": {    "apify": {      "url": "https://mcp.apify.com/?tools=actors,docs,johnvc/linkedin-posts-api",      "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" }    }  }}
  1. Open Cursor → Settings → MCP and confirm the apify server is connected (green dot).
  2. In Composer or Chat, ask Cursor to call the LinkedIn Posts API.

New to Cursor? Get it here: https://cursor.com/referral?code=XQP4VBLI3NNX


Install in ChatGPT

[Install in ChatGPT]

ChatGPT connects to the Apify MCP server through Developer mode (available on ChatGPT Pro, Plus, Business, Enterprise, and Education plans).

  1. Click your profile icon, then go to Settings > Apps. If you do not see a Create app button, open Advanced settings and enable Developer mode.
  2. Click Create app and fill out the form:
    • Name: Apify
    • MCP Server URL: https://mcp.apify.com/?tools=actors,docs,johnvc/linkedin-posts-api
    • Authentication: OAuth
  3. Click Create and authorize the connection with Apify.
  4. To use the app in a conversation, click + in the chat, choose Developer mode, and select Apify.

More help: https://docs.apify.com/platform/integrations/mcp


Made with care

Use the LinkedIn Posts API to power your content research, social listening, and engagement analytics with reliable, structured results.

🤖 Ask an AI assistant about this Actor

Open a ready-to-send prompt about the LinkedIn Posts API in the AI of your choice:

Last Updated: 2026.10.27

來源:README.md,提交 fe3daf3

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版本歷史

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  1. v1.0.0最新Sep 16, 2026

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