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postforged-analytics-mcp

v0.1.0

Published

MCP server for LinkedIn post analytics — score content quality, predict engagement, analyze posting patterns, benchmark performance, and optimize content strategy

Readme

postforged-analytics-mcp

LinkedIn post analytics MCP server — score content quality, predict engagement, analyze posting patterns, benchmark performance, and optimize content strategy.

Tools

| Tool | Description | |------|-------------| | score_post_quality | Score a LinkedIn post for readability, hook strength, CTA effectiveness, and overall engagement potential | | predict_engagement | Predict likes, comments, shares, and impressions based on content and audience | | analyze_posting_pattern | Analyze posting frequency, timing, and content mix for optimization | | benchmark_performance | Compare your performance against industry benchmarks by audience size | | optimize_content_strategy | Generate a data-driven content calendar and growth strategy |

Installation

npx postforged-analytics-mcp

Configuration

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "postforged-analytics": {
      "command": "npx",
      "args": ["-y", "postforged-analytics-mcp"]
    }
  }
}

Environment Variables (Optional)

POSTFORGED_API_URL=https://postforged.com
POSTFORGED_API_KEY=your-api-key

Examples

Score a Post

Use the score_post_quality tool to analyze:
"Stop writing LinkedIn posts about being excited to announce things.

Nobody cares about your announcement. They care about what's in it for them.

Here's what top 1% of LinkedIn creators do instead:

1. Lead with a contrarian take
2. Share specific numbers
3. End with a question

What's the worst LinkedIn opener you've seen?"

Predict Engagement

Use predict_engagement with followerCount: 5000, format: "carousel", industry: "tech"

License

MIT — postforged.com