Linkedin Posts

io.github.johnisanerd由 GitHub 账号 johnisanerd 发布v1.0.0更新于 Oct 26, 2026

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

发布方自己的服务:posts.design

已验证Streamable HTTP可网页运行网页搜索与抓取沟通与协作数据与分析

概览

AI 生成的概览

由 Apify 托管的 MCP 服务器,可将公开的 LinkedIn 帖子抓取为结构化 JSON,包含正文、互动数据和作者信息。

可以做什么
  • 按个人主页链接发现该用户近期发布的公开帖子,可限制数量并按日期筛选
  • 按帖子链接直接抓取指定帖子,每次最多 1000 条
  • 每条帖子返回一行格式一致的 JSON,含正文、话题标签、媒体、链接、帖子类型和日期
  • 提供点赞、评论、分享数,以及作者姓名、头衔、粉丝数和部分热门评论
  • 每行附带通俗的 summary 字段,无公开帖子的主页会返回错误行而非中断整批任务
适用场景
适合对公开 LinkedIn 内容做内容调研、社交聆听和互动数据分析,或让助手把某人的近期帖子直接整理进表格、数据库或 BI 工具,而无需自建采集设施。
运行要求
需要 mcp.apify.com 上的远程 MCP 端点并通过 MCP 客户端连接;需要 Apify 账号,可用 OAuth 登录或以 Bearer Authorization 头提供 Apify API 令牌。README 中的本地示例还要求 Python 3.11 及以上,并设置 APIFY_API_TOKEN 环境变量。
安装前请注意
按返回的帖子数量计费,大批量运行会产生费用,建议限制每个主页的帖子数以控制开销。该服务只读取公开的 LinkedIn 页面,不索取 LinkedIn 账号或会话 Cookie,但仍需你的 Apify 账号或令牌,且请求在 Apify 的服务器上执行而非本机。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Linkedin Posts,将其添加到工作区。
  2. 连接 所需的 凭据。它们会被私密存储,绝不会在公开页面上展示。
  3. 为需要使用其工具的对话启用该服务。

Web executable,通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体,或 SourceWeft 对话中。智能体会读取该服务器的安装指南,先向你展示发布者、端点和登录方式,经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.johnisanerd%2Flinkedin-posts/install.md,并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前,先告诉我它的发布者、端点和登录方式,等我确认。不要把 API 密钥写进命令或我会提交的文件;问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。 客户端会在首次连接时完成 OAuth 登录。

{
  "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

来源:README.md,提交 fe3daf3

工具

0
该服务器需要登录,因此 SourceWeft 无法在没有账号的情况下读取其工具,也未收录。已连接账号所能看到的内容可能因账号及其权限而异。

版本历史

1
  1. v1.0.0最新Sep 16, 2026

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