Inkognito

Privacy-first document processing FastMCP server. Extract, anonymize, and segment documents through FastMCP's modern tool interface.

Please note: As an MCP, privacy of file contents cannot be absolutely guaranteed, but it is a central design consideration. While file contents should be low risk (but non-zero) risk for leakage, file names will, unavoidably and by design, be read and written by the MCP. Plan accordingly. Consider using a local model.

Quick Start

Installation

# Install via pip
pip install inkognito

# Or via uvx (no Python setup needed)
uvx inkognito

# Or run directly with FastMCP
fastmcp run inkognito

Configure Claude Desktop

If not already present, you need to make sure you add a filesystem MCP.

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "inkognito": {
      "command": "uvx",
      "args": ["inkognito"],
      "env": {
        // Optional: Add keys when extractors are implemented
        // "AZURE_DI_KEY": "your-key-here",
        // "LLAMAPARSE_API_KEY": "your-key-here"
      }
    },
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/Users/you/input-files-or-whatever",
        "/Users/you/output-folder-if-you-want-one"
      ],
      "env": {},
      "transport": "stdio",
      "type": null,
      "cwd": null,
      "timeout": null,
      "description": null,
      "icon": null,
      "authentication": null
    }
  }
}

Basic Usage

In Claude Desktop:

"Extract this PDF to markdown"
"Anonymize all documents in my contracts folder"
"Split this large document into chunks for processing"
"Create individual prompts from this documentation"

Features

🔒 Privacy-First Anonymization

• Universal PII detection (50+ types)
• Consistent replacements across all documents
• Reversible with secure vault file
• No configuration needed - smart defaults

📄 Multiple Extraction Options

• Available Now: Docling (default, with OCR support)
• Planned: Azure DI, LlamaIndex, MinerU (placeholders only)
• Auto-selects best available option
• Falls back to Docling if no cloud options

✂️ Intelligent Segmentation

• Large documents: 10k-30k token chunks
• Prompt generation: Split by headings
• Preserves context and structure
• Markdown-native processing

FastMCP Tools

All tools are exposed through FastMCP's modern interface with automatic progress reporting and error handling.

anonymize_documents

Replace PII with consistent fake data across multiple files.

anonymize_documents(
    directory="/path/to/docs",
    output_dir="/secure/output"
)

extract_document

Convert PDF/DOCX to markdown.

extract_document(
    file_path="/path/to/document.pdf",
    extraction_method="auto"  # auto, docling (others coming soon)
)

segment_document

Split large documents for LLM processing.

segment_document(
    file_path="/path/to/large.md",
    output_dir="/output/segments",
    max_tokens=20000
)

split_into_prompts

Create individual prompts from structured content.

split_into_prompts(
    file_path="/path/to/guide.md",
    output_dir="/output/prompts",
    split_level="h2", #configurable, LLM should be able to read the contents of these files safely
)

restore_documents

Restore original PII using vault.

restore_documents(
    directory="/anonymized/docs",
    output_dir="/restored",
    vault_path="/secure/vault.json"
)

Extractor Status

Extractor	Status	Notes
Docling	✅ Fully Implemented	Default extractor with OCR support (OCRMac on macOS, EasyOCR on other platforms)
Azure DI	⚠️ Placeholder	Requires AZURE_DI_KEY environment variable when implemented
LlamaIndex	⚠️ Placeholder	Requires LLAMAPARSE_API_KEY environment variable when implemented
MinerU	⚠️ Placeholder	Will require magic-pdf library when implemented

Configuration

Following FastMCP conventions, all configuration is via environment variables:

# Optional API keys for cloud extractors (when implemented)
export AZURE_DI_KEY="your-key-here"
export LLAMAPARSE_API_KEY="your-key-here"

# Optional OCR languages (comma-separated, default: all available)
export INKOGNITO_OCR_LANGUAGES="en,fr,de"

Examples

Legal Document Processing

You: "Anonymize all contracts in the merger folder for review"

Claude: "I'll anonymize those contracts for you...

[Processing 23 files...]

✓ Anonymized 23 contracts
✓ Replaced: 145 company names, 89 person names, 67 case numbers
✓ Vault saved to: /output/vault.json

Research Paper Extraction

You: "Extract this 300-page research PDF"

Claude: "I'll extract that PDF to markdown...

[Using Docling for extraction...]

✓ Extracted 300 pages
✓ Preserved: tables, figures, citations
✓ Output size: 487,000 tokens
✓ Saved to: research_paper.md

Documentation to Prompts

You: "Split this API documentation into individual prompts"

Claude: "I'll split the documentation by endpoints...

[Splitting by H2 headings...]

✓ Created 47 prompt files
✓ Each prompt includes endpoint context
✓ Ready for training or testing

Performance

Extractor	Speed	Requirements	Status
Azure DI	0.2-1 sec/page	API key	Planned
LlamaIndex	1-2 sec/page	API key	Planned
MinerU	3-7 sec/page	Local, GPU	Planned
Docling	5-10 sec/page	Local, CPU	✅ Available

Privacy & Security

• Local processing: No cloud services required
• No persistence: Nothing saved without explicit paths
• Secure vaults: Encrypted mapping storage
• API key safety: Never logged or transmitted

Development

Running Locally

# Clone the repository
git clone https://github.com/phren0logy/inkognito
cd inkognito

# Run with FastMCP CLI
fastmcp dev

# Or run directly in development
uv run python server.py

Testing with FastMCP

# Install the server configuration
fastmcp install inkognito

# Test a specific tool
fastmcp test inkognito extract_document

Project Structure

inkognito/
├── pyproject.toml          # FastMCP-compatible packaging
├── LICENSE                 # MIT license
├── README.md               # This file
├── server.py               # FastMCP server and entry point
├── anonymizer.py           # PII detection and anonymization
├── vault.py                # Vault management for reversibility
├── segmenter.py            # Document segmentation
├── exceptions.py           # Custom exceptions
├── extractors/             # PDF extraction backends
│   ├── __init__.py
│   ├── base.py
│   ├── registry.py
│   ├── docling.py          # ✅ Implemented
│   ├── azure_di.py         # Placeholder
│   ├── llamaindex.py       # Placeholder
│   └── mineru.py           # Placeholder
└── tests/

License

MIT License - see LICENSE file for details.