MCP For Claude Marketplace
LinkedIn Document Carousel Parser

LinkedIn Document Carousel Parser MCP Connector for Claude

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Analyze LinkedIn PDF carousels for readability, visual rhythm, and engagement.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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This MCP server provides a specialized toolkit for auditing LinkedIn document carousels. It allows AI agents to parse raw text into individual slides using extract_slides, identify overcrowded pages with analyze_density, assess content consistency via evaluate_rhythm, and quantify engagement potential through detect_hooks. Perfect for creators ensuring their PDF carousels are optimized for mobile consumption and high swipe-through rates.

linkedincarouselseoengagementparsing

4 tools expose this connector's capabilities to your AI agent.

analyze_density

Identifies slides that exceed word or character limits

detect_hooks

Quantifies engagement-driving language at the end of slides

evaluate_rhythm

Assesses the consistency of content length across slides

extract_slides

Returns an array of slides. Extracts individual slides from a raw text stream

See how to talk to your AI agent using LinkedIn Document Carousel Parser.

Parse this text and tell me if any slides are too long: '[Slide 1] Welcome to the guide! --- [Slide 2] This slide is way too long because it contains a massive amount of text that definitely exceeds the three hundred character limit for optimal mobile reading.'

Slide 2 is flagged as congested because it exceeds the 300-character threshold.

Check the engagement of these slides: '[Slide 1] Here is a secret... [Slide 2] ...that you won't believe!'

The engagement score is high due to the detection of cliffhanger words like 'secret' and 'won't believe'.

Analyze the rhythm of this carousel: '[Slide 1] Short. [Slide 2] Medium length slide here. [Slide 3] Very long and detailed content that might disrupt the visual flow.'

The rhythm is unstable due to high variance in word counts across the slides.

Provide your carousel text as a single string. The tool looks for markers like [Slide 1] or --- to identify where each slide begins.

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