Li Data Scraper MCP Server
# Li Data Scraper MCP Server
[English](./README_EN.md) | 简体中文 | [繁體中文](./README_ZH-TW.md)
用于访问 Li Data Scraper API 的 MCP 服务器。
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---
## 简介
这是一个 MCP 服务器,用于访问 Li Data Scraper API。
- **PyPI 包名**: `bach-li_data_scraper`
- **版本**: 1.0.0
- **传输协议**: stdio
## 安装
### 从 PyPI 安装:
```bash
pip install bach-li_data_scraper
```
### 从源码安装:
```bash
pip install -e .
```
## 运行
### 方式 1: 使用 uvx(推荐,无需安装)
```bash
# 运行(uvx 会自动安装并运行)
uvx --from bach-li_data_scraper bach_li_data_scraper
# 或指定版本
uvx --from bach-li_data_scraper@latest bach_li_data_scraper
```
### 方式 2: 直接运行(开发模式)
```bash
python server.py
```
### 方式 3: 安装后作为命令运行
```bash
# 安装
pip install bach-li_data_scraper
# 运行(命令名使用下划线)
bach_li_data_scraper
```
## 配置
### API 认证
此 API 需要认证。请设置环境变量:
```bash
export API_KEY="your_api_key_here"
```
### 环境变量
| 变量名 | 说明 | 必需 |
|--------|------|------|
| `API_KEY` | API 密钥 | 是 |
| `PORT` | 不适用 | 否 |
| `HOST` | 不适用 | 否 |
### 在 Cursor 中使用
编辑 Cursor MCP 配置文件 `~/.cursor/mcp.json`:
```json
{
"mcpServers": {
"bach-li_data_scraper": {
"command": "uvx",
"args": ["--from", "bach-li_data_scraper", "bach_li_data_scraper"],
"env": {
"API_KEY": "your_api_key_here"
}
}
}
}
```
### 在 Claude Desktop 中使用
编辑 Claude Desktop 配置文件 `claude_desktop_config.json`:
```json
{
"mcpServers": {
"bach-li_data_scraper": {
"command": "uvx",
"args": ["--from", "bach-li_data_scraper", "bach_li_data_scraper"],
"env": {
"API_KEY": "your_api_key_here"
}
}
}
}
```
## 可用工具
此服务器提供以下工具:
### `get_public_profile_data_by_url`
Enrich public profile data
**端点**: `GET /get-profile-data-by-url`
---
### `get_company_details`
The endpoint returns enrich company details
**端点**: `GET /get-company-details`
---
### `get_company_by_domain`
Enrich company data by domain. **1 credit per successful request.**
**端点**: `GET /get-company-by-domain`
---
### `search_people`
You may see less than 10 results per page. This is because not all profiles as public, sometimes hiding profiles. The endpoint automatically filters these profiles from the result
**端点**: `GET /search-people`
---
### `about_the_profile`
Get profile verification details, profile’s joined, contact information updated, and profile photo updated date
**端点**: `GET /about-this-profile`
---
### `get_profile_data_and_connection_u0026_follower_count`
Get Profile Data and Connection \u0026 Follower Count
**端点**: `GET /data-connection-count`
---
### `get_post_comment_reaction`
Get post comment Reaction
**端点**: `POST /posts/comments/reactions`
---
### `search_post_by_keyword`
Search Post by Keyword
**端点**: `POST /search-posts`
---
### `get_post_reactions`
Get profiles that reacted to the post
**端点**: `POST /get-post-reactions`
---
### `get_profile_post_and_comments`
Get profile post and comments of the post
**端点**: `GET /get-profile-post-and-comments`
---
### `get_profiles_comments`
Get last 50 comments of a profile. 1 credit per call
**端点**: `GET /get-profile-comments`
---
### `get_company_jobs`
Get company jobs
**端点**: `POST /company-jobs`
---
### `ping`
Ping
**端点**: `GET /health`
---
### `get_profile_recent_activity_time`
Get the time of the profile's last activity
**端点**: `GET /get-profile-recent-activity-time`
---
### `get_profile_reactions`
Find out what posts a profile reacted to
**端点**: `GET /get-profile-likes`
---
### `get_profile_post_comment`
Get 50 comments of a profile post (activity)
**端点**: `GET /get-profile-posts-comments`
---
### `get_profiles_posts`
Get last 50 posts of a profile. 1 credit per call
**端点**: `GET /get-profile-posts`
---
### `search_post_by_hashtag`
Search Post by Hashtag
**端点**: `POST /search-posts-by-hashtag`
---
### `get_company_post_comments`
Get comments of a company post
**端点**: `GET /get-company-post-comments`
---
### `get_companys_post`
Get last 50 posts of a company. 1 credit per call
**端点**: `GET /get-company-posts`
---
## 技术栈
- **传输协议**: stdio
- **HTTP 客户端**: httpx
## 许可证
MIT License - 详见 [LICENSE](./LICENSE) 文件。
## 开发
此服务器由 [API-to-MCP](https://github.com/BACH-AI-Tools/api-to-mcp) 工具生成。
版本: 1.0.0
TDQS
Scored across 20 tools
There is significant overlap between tools targeting similar resources, such as get_profile_post_and_comments, get_profile_post_comment, and get_profiles_comments, which could confuse an agent about which to use for profile comments. However, descriptions help differentiate some tools, like distinguishing company vs. profile operations, preventing complete ambiguity.
Naming is inconsistent with mixed patterns: most tools use get_* or search_* prefixes, but about_the_profile deviates, and some names are overly verbose or include escaped characters (e.g., get_profile_data_and_connection_u0026_follower_count). This lack of a uniform convention reduces predictability and readability.
With 20 tools, the count is borderline high for a data scraper server, feeling slightly heavy but not extreme. It covers multiple domains (profiles, companies, posts, searches), which justifies some volume, but could benefit from consolidation to avoid overlap and improve coherence.
The tool set provides broad coverage for LinkedIn data scraping, including profile, company, post, comment, reaction, and search operations, with minor gaps such as missing update or delete tools (expected for read-only scraping) and no direct tool for managing credits or errors. Overall, it supports core workflows without major dead ends.