rag-chunk-boundary-optimizer Connector for AI agents.
3 live capabilities
Optimize RAG chunking strategies for better retrieval accuracy
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Why people use rag-chunk-boundary-optimizer
Fixing broken RAG retrieval with rag-chunk-boundary-optimizer
This MCP changes that. Instead of hunting for errors, you let your agent audit the data. You get clear, actionable metrics on whether your chunks are structurally sound and semantically coherent. It turns a guessing game into a precise engineering task.
What Vinkius changes
You get a mathematical way to prove your RAG data is actually ready for retrieval.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Fixing broken context in long documents
An engineer notices their agent is missing facts from long PDFs.
- Real-world use case 02
Validating new chunking parameters
A developer changes the chunk size from 512 to 256 tokens.
- Real-world use case 03
Detecting data loss in pipelines
A data scientist suspects their preprocessing script is dropping characters.
Complete set · 3capabilities
The complete rag-chunk-boundary-optimizer capability set.
These are the exact actions your AI can choose when you ask it to work with rag-chunk-boundary-optimizer.
01—03
3 capabilities in this set.
Part of 3 available through rag-chunk-boundary-optimizer.
- 01 Capability
Analyze chunk boundaries
Evaluates a sequence of text chunks to determine their structural and semantic quality. It helps you see if your splits make sense linguistically.
- 02 Capability
Get chunking summary
Provides high-level statistical insights into the quality of an entire chunking strategy. Use this to get a bird's-eye view of your dataset's health.
- 03 Capability
Validate overlap integrity
Checks if the character overlap is consistent or if there are gaps between chunks. It prevents data loss during the splitting process.
Set up in minutes
One URL. Then ask rag-chunk-boundary-optimizer to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use rag-chunk-boundary-optimizer 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_f3mbb934ODmYj40cpYR2BbY8BQOqdDe7gJEWXZfQ/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 rag-chunk-boundary-optimizer, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable rag-chunk-boundary-optimizer for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_f3mbb934ODmYj40cpYR2BbY8BQOqdDe7gJEWXZfQ/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 rag-chunk-boundary-optimizer URL.
- Step 03
Save and start
Save the connection and enable rag-chunk-boundary-optimizer in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"rag-chunk-boundary-optimizer-1": {
"url": "https://edge.vinkius.com/vk_preview_f3mbb934ODmYj40cpYR2BbY8BQOqdDe7gJEWXZfQ/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 rag-chunk-boundary-optimizer
Open Agent mode in chat and ask: "Using rag-chunk-boundary-optimizer, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"rag-chunk-boundary-optimizer-1": {
"url": "https://edge.vinkius.com/vk_preview_f3mbb934ODmYj40cpYR2BbY8BQOqdDe7gJEWXZfQ/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 rag-chunk-boundary-optimizer
Ask Copilot: "Using rag-chunk-boundary-optimizer, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"rag-chunk-boundary-optimizer-1": {
"url": "https://edge.vinkius.com/vk_preview_f3mbb934ODmYj40cpYR2BbY8BQOqdDe7gJEWXZfQ/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 rag-chunk-boundary-optimizer
Open Cascade and ask: "Using rag-chunk-boundary-optimizer, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"rag-chunk-boundary-optimizer-1": {
"url": "https://edge.vinkius.com/vk_preview_f3mbb934ODmYj40cpYR2BbY8BQOqdDe7gJEWXZfQ/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 rag-chunk-boundary-optimizer
Ask Cline: "Using rag-chunk-boundary-optimizer, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add rag-chunk-boundary-optimizer-1 --transport http "https://edge.vinkius.com/vk_preview_f3mbb934ODmYj40cpYR2BbY8BQOqdDe7gJEWXZfQ/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 rag-chunk-boundary-optimizer
Ask Claude: "Using rag-chunk-boundary-optimizer, show me...". 3 tools are ready
Where the request belongs
Work rag-chunk-boundary-optimizer can move forward.
This is for the engineers and data scientists building production-grade RAG systems who are tired of seeing their agents hallucinate due to poor context retrieval.
AI Engineer
Optimizing chunking strategies to improve retrieval precision and recall.
NLP Researcher
Analyzing how different splitting methods affect semantic continuity.
Data Engineer
Validating that data preprocessing pipelines aren't losing information during partitioning.
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Bring your own AI
Change the model, client or framework. Keep rag-chunk-boundary-optimizer connected.
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Before you connect
Questions about rag-chunk-boundary-optimizer.
The practical details behind the request, access and result.
How can I use rag-chunk-boundary-optimizer to improve my RAG accuracy?
You use it to identify where your text splitting is breaking sentences or losing semantic meaning. Fixing these boundary issues ensures your agent retrieves complete, coherent context, which directly reduces hallucinations.
Can rag-chunk-boundary-optimizer find missing text in my data?
Yes. It can detect gaps between your text segments, helping you ensure that your chunking process isn't accidentally dropping characters or words during the split.
Is rag-chunk-boundary-optimizer useful for large datasets?
Absolutely. Instead of manually checking chunks, you can get aggregate statistical summaries of your entire chunking strategy to see how it performs across thousands of segments.
Does rag-chunk-boundary-optimizer work with any text splitter?
Yes. It is designed to analyze the output of your existing chunking logic, regardless of whether you use character-based, token-based, or recursive splitting.
How does rag-chunk-boundary-optimizer help with agent hallucinations?
Hallucinations often happen when an agent receives fragmented or incomplete context. By ensuring your chunks don't break mid-sentence and maintain semantic continuity, you provide much cleaner data to your agent.
How can I check if my chunks are breaking sentences?
You can use the analyze_chunk_boundaries capability, which returns an isMidSentence boolean for every boundary evaluated.
What is the purpose of the continuity score?
The continuity score is a proxy for semantic integrity. It measures the density of pronouns and conjunctions at the boundary to identify if a logical connection is being severed.
How do I get a high-level overview of my chunking strategy?
Use the get_chunking_summary capability to receive aggregate metrics like average overlap and mid-sentence rates for your entire set of chunks.
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