DocSlicer MCP server

DocSlicer

Lightning-fast (31 pages/sec), deterministic document parser and chunker for business documents. No LLM calls or heavy ML models.

DocSlicer turns PDFs, Word documents, HTML pages, and PowerPoint files into clean chunks, structured blocks, tables, charts, markdown and a navigable heading hierarchy.

Top score on BizDocBench (0.88 overall vs 0.70 for the next-best tool). 0.80 table accuracy, 0.98 content faithfulness, 0.85 heading recognition and hierarchy preservation, and 0.76 RAG retrieval performance.

Two ways to use it:

• As a Python library — classic RAG. The layout-aware chunker gives you clean, non-overlapping chunks, each carrying its full heading breadcrumb, ready to embed. Jump to the API ↓
• As an MCP server — vectorless RAG. For when you want an answer out of a document right now. Claude, Cursor, or VS Code pulls the outline, picks the section it needs, and reads only that — no embedding, and no 200-page document in the context window. Jump to setup ↓

Quick start

import docslicer

def main():
    result = docslicer.parse_document("annual_report.pdf")

    # Inspect the outline first
    result.hierarchy.to_outline()
    # - PART I — FINANCIAL INFORMATION
    #   - Item 1. Financial Statements
    #     - Notes to Condensed Consolidated Financial Statements
    #       - Note 4 – Financial Instruments
    #         - Derivative Instruments and Hedging
    #           - Foreign Exchange Rate Risk
    #           - Interest Rate Risk
    #         - Accounts Receivable
    #           - Trade Receivables
    #   - Item 2. Management's Discussion and Analysis
    #     - Liquidity and Capital Resources
    # - PART II — OTHER INFORMATION
    #   ...

    # Pull only the chunks you need
    risk_section = result.find_heading("Risk Factors")[0]
    chunks = result.chunks_under(risk_section)

    # Tables come back structured, not as flat text
    for table in result.tables_under(risk_section):
        print(table.markdown)

if __name__ == "__main__":
    main()

Features

Shortened. The full README is on GitHub.