Subtitle Readability Pacer Connector for AI agents.
3 live capabilities
Create perfectly timed captions that meet human readability standards for video content.
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Why people use Subtitle Readability Pacer
Subtitle Readability Pacer for High-Quality Video Captioning
This Connector changes that. You just give your agent the text, and it handles the math of slicing it into perfectly timed blocks. You get a clean list of timestamps and character counts that follow professional standards every single time.
What Vinkius changes
You get perfectly paced captions that won't leave your audience confused or frustrated.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Handling fast-talking guests in interviews
Use calculate_speaking_rate to see the CPS and generate_subtitle_blocks to create more frequent, shorter updates for fast-paced dialogue.
- Real-world use case 02
Fixing dense captions in corporate training
Run check_readability_risk on your current draft to see which parts need to be broken down to meet accessibility standards.
- Real-world use case 03
Automating captions for long-form content
Give your agent the transcript and let it generate the timestamped blocks automatically to save hours of manual timing.
Complete set · 3capabilities
The complete Subtitle Readability Pacer capability set.
These are the exact actions your AI can choose when you ask it to work with Subtitle Readability Pacer.
01—03
3 capabilities in this set.
Part of 3 available through Subtitle Readability Pacer.
- 01 Capability
Check readability risk
Use this to see if a subtitle block is going to be hard for people to read. It flags segments that violate standard readability rules.
- 02 Capability
Calculate speaking rate
This capability figures out how many characters are spoken per second in a specific text segment. It helps you know if the pace is natural or way too fast for a viewer to keep up.
- 03 Capability
Generate subtitle blocks
This capability takes a long string of text and cuts it into multiple timestamped blocks. It ensures every block stays under the character limit and fits on the screen properly.
Set up in minutes
One URL. Then ask Subtitle Readability Pacer to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Subtitle Readability Pacer from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_a26ll7OBqreO3wLYmK6UBPmdn6AfcHoeptdmuWBk/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Subtitle Readability Pacer, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Subtitle Readability Pacer for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_a26ll7OBqreO3wLYmK6UBPmdn6AfcHoeptdmuWBk/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Subtitle Readability Pacer URL.
- Step 03
Save and start
Save the connection and enable Subtitle Readability Pacer in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"subtitle-readability-pacer": {
"url": "https://edge.vinkius.com/vk_preview_a26ll7OBqreO3wLYmK6UBPmdn6AfcHoeptdmuWBk/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Subtitle Readability Pacer
Open Agent mode in chat and ask: "Using Subtitle Readability Pacer, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"subtitle-readability-pacer": {
"url": "https://edge.vinkius.com/vk_preview_a26ll7OBqreO3wLYmK6UBPmdn6AfcHoeptdmuWBk/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Subtitle Readability Pacer
Ask Copilot: "Using Subtitle Readability Pacer, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"subtitle-readability-pacer": {
"url": "https://edge.vinkius.com/vk_preview_a26ll7OBqreO3wLYmK6UBPmdn6AfcHoeptdmuWBk/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Subtitle Readability Pacer
Open Cascade and ask: "Using Subtitle Readability Pacer, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"subtitle-readability-pacer": {
"url": "https://edge.vinkius.com/vk_preview_a26ll7OBqreO3wLYmK6UBPmdn6AfcHoeptdmuWBk/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Subtitle Readability Pacer
Ask Cline: "Using Subtitle Readability Pacer, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add subtitle-readability-pacer --transport http "https://edge.vinkius.com/vk_preview_a26ll7OBqreO3wLYmK6UBPmdn6AfcHoeptdmuWBk/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Subtitle Readability Pacer
Ask Claude: "Using Subtitle Readability Pacer, show me...". 3 tools are ready
Where the request belongs
Work Subtitle Readability Pacer can move forward.
This is for video editors and captioning specialists who are tired of manually dragging text blocks on a timeline and want to ensure their content is accessible to everyone.
Video Editor
Uses this to automate the tedious process of timing captions for long-form content like interviews or tutorials.
Captioning Specialist
Uses this to maintain strict quality and accessibility standards across high-volume production pipelines.
Accessibility Coordinator
Uses this to verify that all corporate training videos meet strict readability requirements for the hearing impaired.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
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Deterministic Readability Scorer
Equip your AI with strict linguistic math. Calculate Flesch-Kincaid, Gunning Fog indexes, and exact reading times deterministically.
Text Readability Scorer
Calculate mathematically accurate readability metrics (Flesch-Kincaid, Gunning Fog, SMOG) for any text. Stop relying on AI 'feelings'. get exact US grade levels for SEO and compliance.
Cognitive Load Scorer
Quantify the mental effort required to process text by measuring linguistic complexity.
Readability Score Analyzer
Analyze text complexity using standard linguistic formulas like Flesch-Kincaid and SMOG.
Bring your own AI
Change the model, client or framework. Keep Subtitle Readability Pacer connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Subtitle Readability Pacer.
The practical details behind the request, access and result.
What does Subtitle Readability Pacer do for my videos?
It calculates the best way to split your captions so they are easy to read. It handles the math of timing and character limits for you.
Can Subtitle Readability Pacer help with accessibility?
Yes. It ensures your captions do not move too fast and stay within character limits, making them much easier for everyone to follow.
How does Subtitle Readability Pacer handle long sentences?
It automatically slices long strings of text into multiple, timestamped blocks that fit on the screen properly.
Will Subtitle Readability Pacer make my captions look professional?
Yes, it enforces a strict 42-character limit per line, which is a standard for clean, professional-looking captions.
Can I use Subtitle Readability Pacer for fast-talking speakers?
Yes, it calculates the speaking rate to help you identify if a segment is too fast and suggests how to break it down.
Does Subtitle Readability Pacer work with any AI client?
You can use it with any MCP-compatible client like Claude, Cursor, or Windsurf to manage your captioning workflow.
How does the capability determine if a subtitle is readable?
The capability uses check_readability_risk to evaluate the Characters Per Second (CPS). If the rate exceeds 15 CPS, it flags a high risk level and suggests fragmentation.
Can I split long segments into multiple blocks?
Yes, the generate_subtitle_blocks capability automatically decomposes heavy segments into multiple timestamped blocks that satisfy character and duration constraints.
What are the character limits per line?
To prevent eye strain, the engine ensures no single line exceeds 42 characters and no block contains more than two lines.
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