MyAIGist MCP Server

MCP server providing document intelligence and knowledge management for Claude Desktop and other MCP-compatible clients. Process documents, answer questions with RAG, and maintain a persistent knowledge base - all running locally.

Overview

MyAIGist MCP provides 10 powerful tools for document intelligence and knowledge management:

• Document Processing: PDF, DOCX, TXT, URLs, file attachments, and batch processing
• Q&A System: RAG-powered question answering across multiple documents
• Knowledge Management: Persistent vector storage with document tracking

Features

✅ MCP-compatible - Works with Claude Desktop, Cursor, and other MCP clients ✅ Claude-powered - Uses Claude Sonnet 4.5 for summarization and Q&A ✅ Local execution - Runs on your machine with no external infrastructure ✅ Persistent storage - Single vector store survives across sessions ✅ Multi-document RAG - Unlimited documents in knowledge base ✅ Simple setup - Install dependencies, configure API keys, and go

Installation

Prerequisites

• Python 3.8+
• Anthropic API key (for Claude text generation)
• OpenAI API key (for embeddings)
• MCP-compatible client (Claude Desktop, Cursor, etc.)

Setup

1. Install dependencies:

   cd /path/to/myaigist_mcp
   pip install -r requirements.txt
   

2. Configure environment:

   cp .env.example .env
   # Edit .env and add both API keys:
   # - ANTHROPIC_API_KEY (for Claude)
   # - OPENAI_API_KEY (for embeddings)
   

3. Configure your MCP client:

   Claude Desktop - Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

   {
     "mcpServers": {
       "myaigist": {
         "command": "python3",
         "args": ["/path/to/myaigist_mcp/server.py"]
       }
     }
   }
   

   Cursor - Add to your Cursor MCP settings:

   {
     "mcpServers": {
       "myaigist": {
         "command": "python3",
         "args": ["/path/to/myaigist_mcp/server.py"]
       }
     }
   }
   

   Other MCP clients - Refer to your client's documentation for MCP server configuration.

4. Restart your MCP client

   The MCP server will start automatically when you open your client.

Architecture

myaigist_mcp/               # MCP server (this project)
├── server.py               # Main MCP server with 10 tools
├── mcp_agents/             # All agent code (local, self-contained)
│   ├── document_processor.py  # PDF/DOCX/TXT extraction
│   ├── summarizer.py          # 3-level summarization (Claude)
│   ├── embeddings.py          # OpenAI embeddings
│   ├── url_crawler.py         # Web content extraction
│   ├── claude_client.py       # Anthropic client factory
│   ├── openai_client.py       # OpenAI client factory
│   ├── qa_agent.py            # Q&A with RAG (Claude)
│   └── vector_store.py        # Vector storage
└── data/                   # Persistent vector storage
    └── vector_store.pkl    # Created at runtime

Architecture Notes:

• All agents are self-contained in mcp_agents/
• No external dependencies on other projects
• Single-user design (no session/user isolation)
• Persistent vector storage with unlimited documents
• Claude Sonnet 4.5 for text generation (summarization, Q&A)
• OpenAI for embeddings (RAG)

Available Tools (10 Total)

Content Processing (5 tools)

1. process_document

Process a document from LOCAL FILE PATH (PDF, DOCX, TXT) and add to knowledge base.

Parameters:

• file_path (string, required): LOCAL filesystem path (e.g., /Users/mike/file.pdf)
• title (string, optional): Document title (defaults to filename)
• summary_level (string, optional): quick, standard, or detailed

Example:

"Process /Users/mike/contract.pdf as a detailed summary"

Compatibility: Works with all MCP clients.

2. process_uploaded_document

Process a document attached to Claude Desktop (optimized for Claude Desktop file uploads).

Parameters:

• content (string, required): Text content extracted by Claude
• filename (string, required): Original filename
• summary_level (string, optional): quick, standard, or detailed

Example:

[Attach PDF file in Claude Desktop]
"Process this document with MyAIGist"

Compatibility: Designed for Claude Desktop. Other clients should use process_document with file paths.

3. process_text

Process raw text and add to knowledge base.

Example:

"Process this text: [paste long article]"

Compatibility: Works with all MCP clients.

4. process_url

Crawl web URL, extract content, and add to knowledge base.

Example:

"Process https://example.com/article"

Compatibility: Works with all MCP clients.

5. process_batch

Process multiple files and generate unified summary.

Example:

"Process all files in /Users/mike/research/ and give me a unified summary"

Compatibility: Works with all MCP clients.

Q&A System (1 tool)

6. ask_question

Ask questions about stored documents using RAG.

Example:

"What are the main findings in the research papers?"

Compatibility: Works with all MCP clients.

Document Management (3 tools)

7. list_documents

List all documents in knowledge base with metadata.

Example:

"Show me all my documents"

Compatibility: Works with all MCP clients.

8. delete_document

Delete specific document by ID.

Example:

"Delete document abc123xyz"

Compatibility: Works with all MCP clients.

9. clear_all_documents

Clear entire knowledge base.

Example:

"Clear all my documents"

Compatibility: Works with all MCP clients.

Utility Tools (1 tool)

10. get_status

Get system status and knowledge base statistics.

Example:

"What's my system status?"

Compatibility: Works with all MCP clients.

Common Workflows

File Upload (Claude Desktop)

User: [Attaches PDF to Claude Desktop]
User: "Process this document with MyAIGist"
Claude: ✅ Processed with summary

User: "What are the key points?"
Claude: "The key points are..."

Single Document Q&A (Any MCP Client)

User: "Process /Users/mike/contract.pdf"
AI: ✅ Processed with summary

User: "What are the payment terms?"
AI: "The payment terms are net 30..."

Multi-Document Research (Any MCP Client)

User: "Process these 3 research papers: paper1.pdf, paper2.pdf, paper3.pdf"
AI: ✅ Processed all 3 with unified summary

User: "What are the common findings across all papers?"
AI: "The common findings are..."

Configuration

Environment Variables (.env)

# Anthropic API Key (required - for Claude text generation)
ANTHROPIC_API_KEY=sk-ant-your-key-here
ANTHROPIC_MODEL=claude-sonnet-4-5-20250929

# OpenAI API Key (required - for embeddings)
OPENAI_API_KEY=sk-your-key-here
OPENAI_EMBED_MODEL=text-embedding-3-large

Model Selection

Current Configuration:

• Claude Sonnet 4.5 (claude-sonnet-4-5-20250929) - Summarization and Q&A
• text-embedding-3-large - Higher accuracy for RAG vector search

Why Two API Keys?

• Anthropic Claude: Text generation (summarization, Q&A) - excellent quality
• OpenAI: Embeddings (Claude doesn't provide embeddings)

Storage

Vector Store:

• Path: data/vector_store.pkl
• Format: Pickle with numpy arrays
• Persistence: Survives server restarts
• Capacity: Unlimited documents

API Costs

Usage-based pricing:

• Claude Sonnet 4.5: $3/$15 per 1M tokens (input/output)
• OpenAI Embeddings: $0.13 per 1M tokens

Typical monthly usage:

• 100 documents processed: ~$5-10
• 500 questions answered: ~$2-5
• Total: ~$10-15/month

Troubleshooting

Server won't start

# Check if Python can find dependencies
python3 -c "import mcp; print('✅ MCP installed')"

# Check syntax
python3 -m py_compile server.py

# Check logs (Claude Desktop example)
tail -f ~/Library/Logs/Claude/mcp-server-myaigist.log

Import errors

# Test agent imports
cd /path/to/myaigist_mcp
python3 -c "from mcp_agents.summarizer import Summarizer; print('✅ Imports work')"
python3 -c "from mcp_agents.qa_agent import QAAgent; print('✅ QAAgent works')"

API Key errors

# Check environment variables are set
python3 -c "import os; print('Anthropic:', bool(os.getenv('ANTHROPIC_API_KEY'))); print('OpenAI:', bool(os.getenv('OPENAI_API_KEY')))"

Empty knowledge base after restart

• Check data/vector_store.pkl exists
• Verify file permissions (readable/writable)
• Check for errors in server logs

File upload not working

• Claude Desktop: Use process_uploaded_document for file attachments
• Other clients: Use process_document with local filesystem paths
• See tool descriptions for compatibility details

Development

Running Tests

# Test agent imports
python3 -c "from mcp_agents.qa_agent import QAAgent; qa = QAAgent(); print('✅ QAAgent works')"

# Test document processing
cd /path/to/myaigist_mcp
python3 -c "from mcp_agents.document_processor import DocumentProcessor; dp = DocumentProcessor(); print('✅ DocumentProcessor works')"

Debugging

# Run server manually to see output
python3 /path/to/myaigist_mcp/server.py

Project Structure

myaigist_mcp/
├── server.py              # Main MCP server (10 tools)
├── requirements.txt       # Python dependencies
├── .env                   # Environment variables (not in git)
├── .env.example          # Template
├── README.md             # This file
├── mcp_agents/           # MCP-adapted agents
│   ├── __init__.py
│   ├── claude_client.py   # Anthropic client factory
│   ├── summarizer.py      # Modified: uses Claude
│   ├── qa_agent.py        # Modified: uses Claude, single-user
│   ├── vector_store.py    # Modified: no user filtering
│   ├── document_processor.py
│   ├── embeddings.py
│   ├── url_crawler.py
│   └── openai_client.py
└── data/                 # Persistent storage
    └── vector_store.pkl  # Vector embeddings and metadata

MCP Client Compatibility

Tested Clients

• ✅ Claude Desktop - Full support including file attachments
• ⚠️ Cursor - Core functionality works (use file paths instead of attachments)
• ❓ Other MCP clients - Should work with file path-based tools

Compatibility Notes

• All tools work with standard file paths
• process_uploaded_document is optimized for Claude Desktop's file attachment behavior
• Other clients should use process_document with local file paths
• Standard MCP protocol (stdio transport, JSON-RPC)

License

MIT License

Support

For issues or questions:

1. Check troubleshooting section above
2. Review MCP server logs (location varies by client)
3. Verify environment variables in .env
4. Test agent imports individually

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Last Updated: 2026-01-19 Project Status: ✅ Production ready - 10 core tools implemented and tested Models: Claude Sonnet 4.5 (text) + OpenAI (embeddings) Focus: Document intelligence (text-based content only) Compatibility: Claude Desktop, Cursor, and other MCP-compatible clients