How does this differ from a standard text splitter?

Standard splitters often cut text at arbitrary character counts. This MCP uses Markdown headers and paragraph breaks to keep related information together.

What happens if a section is too long?

If a section exceeds your defined token limit, the engine splits the content at paragraph boundaries to maintain semantic integrity.

Which AI clients can use this MCP?

You can use this with any MCP-compatible client, including Claude, Cursor, and Windsurf.

Does it support all Markdown header levels?

Yes, it recognizes and uses the hierarchy for headers ranging from level one through level six.

How do I see the document structure?

You can use the get_header_structure capability to extract and preview the hierarchy of any Markdown file.

How does the chunking process maintain context?

The capability assigns a hierarchical path to every chunk, representing its position in the document tree (e.g., Parent > Child). This ensures that even when text is split, the structural lineage remains attached to the content.

What happens if a section is too large for the token limit?

If a header group exceeds the maxChunkSizeTokens, the engine identifies paragraph boundaries marked by double newlines and splits the content at these points to create smaller, valid chunks.

Can I preview the document structure before chunking?

Yes, you can use the get_header_structure capability to extract and view the identified heading hierarchy without performing the full chunking operation.

An astronaut overseeing a futuristic data hub

Download PDF Markdown Version

Connect Markdown Semantic Chunker to ChatGPT and Claude

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Keep document structure intact for better RAG performance.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 3 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-03

3 capabilities in this set.

Part of 3 available through Markdown Semantic Chunker.

01
Generate semantic chunks
This capability creates logically grouped text segments from your Markdown files. It follows the header hierarchy to ensure each chunk stays meaningful.
02
Get header structure
Use this to pull a map of all headers from a document. It lets you preview the organizational hierarchy before you start chunking.
03
Calculate markdown token density
This capability measures the token density of your Markdown content. It helps you understand how much information is packed into your text blocks.

Real work

What teams actually run

RAG Pipeline Optimization

Improve the accuracy of your agent's answers by providing contextually complete chunks.

Technical Documentation Analysis

Process complex manuals where the relationship between headers and text is critical.

Data Pre-processing

Clean up large Markdown repositories before indexing them into a vector store.

Document Auditing

Check the token density and header depth of your files to plan your embedding strategy.

One connector, every AI

Markdown Semantic Chunker works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

Building your own app? The connector is yours to use.

You don't need a client to put Markdown Semantic Chunker to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

579ms average. Fast in production.

Markdown Semantic Chunker is checked daily against the live service.

Daily averagePeak 690ms

Oct 3Today

Fastest day: 525ms
Slowest day: 690ms
14-day trend: Improving-13%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 3 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Markdown Semantic Chunker, so you can see the experience inside your AI.

It does not authenticate your account with Markdown Semantic Chunker. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Markdown Semantic Chunker Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_7rlgv7c4pKsFI7OUQzClGBELy5KOHUclBVDqK0KP/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — Markdown Semantic Chunker capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "markdown-semantic-chunker-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_7rlgv7c4pKsFI7OUQzClGBELy5KOHUclBVDqK0KP/mcp"
    }
  }
}

Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Markdown Semantic Chunker in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Markdown Semantic Chunker owners hand off.

This MCP is built for engineers and researchers building RAG systems that require high precision. It's for anyone tired of messy, fragmented data being fed into their agents.

01
AI Engineers
They use this to prepare clean, structured datasets for vector databases.
02
Data Scientists
They use it to analyze the density and structure of large document collections.
03
LLM Developers
They use it to ensure their agents have the right context for complex queries.

FAQ

Questions Markdown Semantic Chunker owners ask.

01
How does this differ from a standard text splitter?
Standard splitters often cut text at arbitrary character counts. This MCP uses Markdown headers and paragraph breaks to keep related information together.
02
What happens if a section is too long?
If a section exceeds your defined token limit, the engine splits the content at paragraph boundaries to maintain semantic integrity.
03
Which AI clients can use this MCP?
You can use this with any MCP-compatible client, including Claude, Cursor, and Windsurf.
04
Does it support all Markdown header levels?
Yes, it recognizes and uses the hierarchy for headers ranging from level one through level six.
05
How do I see the document structure?
You can use the get_header_structure capability to extract and preview the hierarchy of any Markdown file.
06
How does the chunking process maintain context?
The capability assigns a hierarchical path to every chunk, representing its position in the document tree (e.g., Parent > Child). This ensures that even when text is split, the structural lineage remains attached to the content.
07
What happens if a section is too large for the token limit?
If a header group exceeds the maxChunkSizeTokens, the engine identifies paragraph boundaries marked by double newlines and splits the content at these points to create smaller, valid chunks.
08
Can I preview the document structure before chunking?
Yes, you can use the get_header_structure capability to extract and view the identified heading hierarchy without performing the full chunking operation.

Explore

More in Data Processing

Connect Markdown Semantic Chunker to Claude, Cursor, ChatGPT & more

Your AI does the work. We keep it safe and sound.

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-03

Generate semantic chunks

Get header structure

Calculate markdown token density

What teams actually run

RAG Pipeline Optimization

Technical Documentation Analysis

Data Pre-processing

Document Auditing

Building your own app? The connector is yours to use.

Markdown Semantic Chunker Connector

Claude Desktop

AI Engineers

Data Scientists

LLM Developers

How does this differ from a standard text splitter?

What happens if a section is too long?

Which AI clients can use this MCP?

Does it support all Markdown header levels?

How do I see the document structure?

How does the chunking process maintain context?

What happens if a section is too large for the token limit?

Can I preview the document structure before chunking?

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