Doc Distillation MCP Server

A Model Context Protocol server for document distillation with multi-source support, producing dual output: HTML distillation articles and Obsidian notes.

Features

• Multi-source support: Feishu documents, webpages, PDFs, video/podcast transcripts, and local files
• Dual output format: HTML distillation articles + Obsidian notes with frontmatter
• Five-stage workflow: Source extraction → Integrity safeguard → Image filtering → HTML generation → Obsidian generation
• Three-layer image filtering: Automatic rule filtering → Context prediction → Safety net
• Key element detection: Formulas, data, templates, checklists, frameworks, tables, warnings, quotes
• Structure skeleton: Heading-based document outline for integrity verification
• Sync & Async modes: Direct results for small documents, task polling for large ones
• Structured output: Pydantic-validated results with segments, images, and metadata

Quick Start

Install

pip install doc-distillation-mcp

# With dev tools (MCP Inspector, testing, linting)
pip install 'doc-distillation-mcp[dev]'

Run

# Direct run
doc-distillation-mcp

# Or with uvx (no install needed)
uvx doc-distillation-mcp

# Debug with MCP Inspector
mcp dev doc_distillation_mcp.server:mcp

Prerequisites (optional)

For PDF text extraction:

pip install pdfplumber

MCP Client Configuration

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "doc-distillation": {
      "command": "uvx",
      "args": ["doc-distillation-mcp"]
    }
  }
}

Cursor

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "doc-distillation": {
      "command": "uvx",
      "args": ["doc-distillation-mcp"]
    }
  }
}

Trae

Add to Trae MCP settings:

{
  "mcpServers": {
    "doc-distillation": {
      "command": "python3",
      "args": ["-m", "doc_distillation_mcp.server"]
    }
  }
}

Claude Code

claude mcp add doc-distillation -- uvx doc-distillation-mcp

Tools

distill_url

Distill content from a URL into an HTML article + Obsidian note.

# Webpage (sync mode - direct result)
distill_url(url="https://example.com/article")

# With Obsidian subdirectory
distill_url(
    url="https://example.com/deep-dive",
    obsidian_subdir="飞书蒸馏"
)

# Large document (async mode - returns task_id)
distill_url(
    url="https://example.com/long-report.pdf",
    async_mode=True
)
# Then poll:
get_distill_status(task_id="abc12345")

Parameters:

Parameter	Type	Default	Description
url	str	required	Document URL (Feishu, webpage, PDF, video)
obsidian_subdir	str?	null	Subdirectory under Obsidian vault
async_mode	bool	false	Return task_id for polling

distill_file

Distill content from a local file.

# Text file (sync mode)
distill_file(file_path="/path/to/notes.txt")

# PDF file with Obsidian subdirectory
distill_file(
    file_path="/path/to/report.pdf",
    obsidian_subdir="PDF蒸馏"
)

# Large file (async mode)
distill_file(
    file_path="/path/to/large.pdf",
    async_mode=True
)

Parameters:

Parameter	Type	Default	Description
file_path	str	required	Path to local file
obsidian_subdir	str?	null	Subdirectory under Obsidian vault
async_mode	bool	false	Return task_id for polling

get_distill_status

Poll the status of an async distillation task.

get_distill_status(task_id="abc12345")
# Returns: {status: "completed", progress: 1.0, result: {...}}

list_distillations

List all completed distillations.

list_distillations()
# Returns: [{task_id, title, source_type, method, segment_count, ...}]

Five-Stage Workflow

URL / File Input
    │
    ├─ Stage 1: Source Detection & Content Extraction
    │   ├─ Feishu: Returns guidance (use lark-doc skill)
    │   ├─ Webpage: HTML parsing (text, headings, images)
    │   ├─ PDF: pdfplumber text extraction
    │   ├─ Video/Podcast: Returns guidance (use video-transcript-mcp)
    │   └─ Local file: Type-based extraction
    │
    ├─ Stage 2: Integrity Safeguard
    │   ├─ Structure skeleton (heading hierarchy)
    │   └─ Key element detection (8 categories)
    │
    ├─ Stage 2.5: Image Filtering (three-layer)
    │   ├─ Layer 1: Automatic rules (size, URL keywords, duplicates, alt keywords)
    │   ├─ Layer 2: Context prediction (nearby text indicates value)
    │   └─ Layer 3: Safety net (near key elements)
    │
    ├─ Stage 3: HTML Distillation Article Generation
    │   └─ Styled HTML with header, content sections, footer
    │
    └─ Stage 4: Obsidian Note Generation
        ├─ Frontmatter (title, source, author, date, tags)
        ├─ Summary callout
        ├─ Content sections
        ├─ Image distillation callouts
        └─ Key element statistics table

Key Element Detection

The distiller detects and counts 8 types of key elements to ensure content completeness:

Element	Description	Example Patterns
formula	Calculation formulas	ROI =, = 销售额, division
data	Numeric data	Percentages, amounts, multiples
template	Templates & scripts	Title formulas, word lists
checklist	Actionable lists	Numbered items, checkboxes
framework	Mental models	Matrices, quadrants, methodologies
table	Tabular data	Markdown tables, comparison
warning	Cautions & pitfalls	"Don't", "Avoid", "Pitfall"
quote	Notable quotes	Long quoted text, key phrases

Environment Variables

Variable	Default	Description
DISTILL_HTML_DIR	~/Documents/蒸馏文稿	HTML output directory
DISTILL_OBSIDIAN_DIR	~/Documents/obsidian	Obsidian vault directory

Supported Sources

Source	URL	Local File	Notes
Webpage	✅	✅	HTML parsing with image extraction
PDF	✅	✅	Requires pdfplumber
Feishu	✅	N/A	Returns guidance (use lark-doc skill)
YouTube	✅	N/A	Returns guidance (use video-transcript-mcp)
Bilibili	✅	N/A	Returns guidance (use video-transcript-mcp)
Douyin	✅	N/A	Returns guidance (use video-transcript-mcp)
Xiaohongshu	✅	N/A	Returns guidance (mini-program)
Text/Markdown	N/A	✅	Direct text extraction
Audio/Video	N/A	✅	Returns guidance (use video-transcript-mcp)

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License

MIT