GenPark RAG Semantic Boundary Chunker
LocalAbout GenPark RAG Semantic Boundary Chunker
GenPark RAG Semantic Boundary Chunker is a text chunking MCP server for workflows that need to preserve document structure. It keeps Markdown headers, code fences, and paragraphs intact while applying token overlap.
Key features
- 01Preserves Markdown headers and paragraphs
- 02Keeps code fences intact
- 03Supports token overlap
- 04Uses only the Python standard library
Use cases
- 01Chunking Markdown for RAG pipelines
- 02Preparing structured text for agent workflows
MCP support
Runs as an MCP server and can be connected to clients such as Claude Desktop, Cursor, and LobeChat. It can also be used directly as a Python library.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEGenPark RAG Semantic Boundary Chunker
Semantic boundary preserving text chunker maintaining markdown headers, code fences, and paragraphs with token overlap.
Part of the GenPark AI Agent Skills Ecosystem.
🌟 Key Features
- 100% Python Standard Library: Zero third-party runtime dependencies.
- MCP Native: Plugs directly into Claude Desktop, Cursor, LobeChat, and autonomous agent DAGs.
- Production Resilience: Strict schema conformance, zero data loss, enterprise hardened.
🚀 Installation & Usage
Run as an MCP server:
python mcp_server.py
Use directly as a library:
from client import *
See example_usage.py for comprehensive integration demonstrations.
📄 License
MIT License. Maintained by GenPark AI.
Comments
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