TradeMCP

Make trade documents machine readable.

Open-source MCP server for document workflow simplification.

--- Built on IBM's Docling for powerful document extraction.

🚀 Works Out of the Box

No AI needed for document processing. No API keys. No cloud dependencies. Works with any AI platform.

This MCP server runs 100% locally using Docling for document extraction - no AI required for the actual processing. Connect it to any MCP-compatible AI assistant:

• Claude Desktop by Anthropic

• Microsoft Copilot via Copilot Studio

• ChatGPT with MCP support

• Any MCP-compatible client (growing ecosystem)

Related MCP server: KnowledgeMCP

🔌 Vendor & Model Agnostic

One engine, any AI platform.

The Model Context Protocol (MCP) is an open standard. This means:

• ✅ Not locked to Anthropic or Claude

• ✅ Works with Microsoft Copilot Studio

• ✅ Compatible with any MCP implementation

• ✅ Future-proof as more platforms adopt MCP

• ✅ Use with GPT, Gemini, Llama, or any model

Your document infrastructure shouldn't depend on a single AI vendor. TradeMCP ensures it doesn't.

🏗️ The Engine, Not the Brain

TradeMCP is the engine that enables document operations:

• Powered by Docling: IBM Research's document parser (no AI needed)

• Deterministic Processing: Same document = same output every time

• MCP Native: Works with any MCP-compatible client

• Zero Configuration: Install and run — no setup required

• 100% Local: Your documents never leave your machine

The brain (workflow intelligence, trade expertise, compliance logic) can come from any AI model or commercial solution - but the engine runs without any AI.

🏗️ Modular Architecture

All components are modular and replaceable. Docling can be replaced with domain-specific tools or services tailored to your exact document processing needs.

📦 Installation & Setup

Prerequisites

1. Install dependencies:

   pip install -r requirements.txt

2. Download the local AI model (first time only):

   python download_models.py

   This downloads a small, efficient AI model that runs 100% locally on your machine.

🧠 About the Local AI Model

What is it?

• Model: sentence-transformers/all-MiniLM-L6-v2

• Size: ~87MB (small and efficient)

• Type: Local embedding model - runs entirely on your CPU/GPU

• Privacy: 100% local - no data sent to external servers

• No API keys: No OpenAI, Anthropic, or cloud service needed

What does it do? This local model provides intelligent document understanding:

• Semantic Search: Find documents by meaning, not just keywords

• Document Similarity: Identify related trade documents automatically

• Smart Categorization: Automatically group similar documents

• Context Understanding: Understand relationships between different parts of documents

Why a local model?

• ✅ Complete Privacy: Your sensitive trade documents never leave your machine

• ✅ No API Costs: No usage fees or rate limits

• ✅ Offline Operation: Works without internet connection

• ✅ Fast Processing: No network latency, instant results

• ✅ Predictable Performance: Same results every time, no service degradation

Note on Model Storage

The model files are stored in model_cache/ (excluded from git to keep the repository lightweight). They persist between sessions - you only download once.

📚 For technical details about the model, see MODEL_INFO.md

Docling by IBM Research (Default Parser)

This project leverages Docling, IBM's advanced document conversion technology:

• Rule-based extraction - No AI/ML required

• Extracts text, tables, and structure from PDFs, DOCX, XLSX, PPTX, and more

• Maintains document layout and formatting intelligence

• Handles complex multi-column layouts and embedded tables

• Open-source (MIT licensed) and actively maintained

• Easily replaceable with custom parsers for specific document types

Model Context Protocol (MCP)

Open standard for AI-tool communication:

• Works with Claude Desktop (Anthropic)

• Compatible with Copilot Studio (Microsoft)

• Supports ChatGPT with MCP integration

• Supports any MCP client implementation

• Vendor-neutral protocol specification

🎯 What This Is (And Isn't)

This IS:

• ✅ A vendor-agnostic MCP server with 14 document tools

• ✅ Deterministic document extraction via Docling (no AI)

• ✅ Full-text and semantic search capabilities

• ✅ Production-ready document processing engine

• ✅ 100% local, offline-capable, no external dependencies

This IS NOT:

• ❌ An AI-powered document processor (it's deterministic)

• ❌ Tied to any specific AI vendor

• ❌ An AI model (it's infrastructure for any AI)

• ❌ A complete workflow automation solution

• ❌ The commercial Kansofy product

⚡ Quick Start

With Claude Desktop

# Add to ~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "trademcp": {
      "command": "python",
      "args": ["/path/to/trademcp/mcp_server.py"]
    }
  }
}

With Microsoft Copilot Studio

# Configure in Copilot Studio as external tool
# Point to the MCP server endpoint
# Use the standard MCP protocol

With ChatGPT (MCP Support)

# Connect via MCP-compatible ChatGPT clients
# Point to the same MCP server endpoint
# Standard MCP protocol compatibility

With Any MCP Client

# Start the MCP server
python mcp_server.py

# Connect any MCP-compatible client
# Server speaks standard MCP protocol

🛠️ What You Get

Document Processing (No AI Required)

# Docling extracts everything deterministically
upload_document("complex_invoice.pdf")
# ✓ Text extracted (rule-based)
# ✓ Tables preserved (pattern matching)
# ✓ Structure maintained (document parsing)
# ✓ Same input = same output every time

Intelligent Search (Still No AI)

# Full-text search with SQLite FTS5
search_documents("payment terms net 30")

# Semantic similarity with pre-computed embeddings
vector_search("documents about shipping delays")

# Find duplicates using hashing
find_duplicates()

MCP Tools for Any AI Assistant

All 14 tools work instantly with any MCP client:

• upload_document - Process any document format (no AI)

• search_documents - Lightning-fast full-text search (SQL)

• vector_search - Find similar documents (embeddings)

• get_document_tables - Extract tables from PDFs (Docling)

• [... and 10 more tools]

🧠 The Brain Lives Elsewhere

This engine provides the infrastructure (no AI). The intelligence comes from:

Your AI Assistant (Claude, Copilot, etc.)

The AI provides the intelligence to:

• Understand your intent

• Orchestrate document operations

• Make decisions based on content

• Generate insights and summaries

Your Own Implementation

Build your own workflows on top:

• Custom document classification

• Business rule validation

• Workflow orchestration

• Integration patterns

Commercial Solutions

Production-ready intelligence:

• Kansofy Trade Cloud: Full SaaS with trade workflows

• Kansofy Enterprise: Self-hosted with compliance engine

• Professional Services: Custom workflow development

📊 How It Works Without AI

Document Processing Pipeline

PDF/DOCX → Docling (rule-based) → Structured Data → SQLite
         ↓
    No AI needed
    Deterministic
    100% reproducible

Search Pipeline

Query → FTS5 (SQL) → Results
      → Embeddings (pre-computed) → Similarity
      
No AI inference at search time

🌐 Platform Compatibility

🤝 Why This Architecture Matters

No AI in the Engine Means:

• Deterministic results (same input = same output)

• No API costs for document processing

• Works offline completely

• No rate limits or quotas

• Full data privacy (nothing leaves your machine)

• Predictable performance

Any AI for the Brain Means:

• Choose your preferred AI assistant

• Switch providers without changing infrastructure

• Use multiple AIs for different tasks

• Future-proof as AI landscape evolves

📈 When You Need More

You'll know it's time for commercial solutions when:

• Processing >100 documents daily

• Need trade-specific workflows

• Require compliance validation

• Want pre-built intelligence

• ROI justifies enterprise features

🔗 Technical Foundation

• Document Processing: Docling by IBM Research (no AI)

• Protocol: Model Context Protocol (open standard)

• Search: SQLite with FTS5 extension (deterministic SQL)

• Embeddings: Sentence-transformers (pre-computed, no inference)

• Server: FastAPI + Python 3.9+ (standard web framework)

📚 Documentation

🙏 Acknowledgements

• IBM Research for Docling

• Anthropic for MCP protocol

• The open-source community

Built with ❤️ for the humans running global trade.
Making trade documents machine readable. The foundation for intelligent trade workflows.