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

Install dependencies:

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

Configure environment:

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

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.

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:

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

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

myaigist_mcp

MyAIGist MCP Server

Overview

Features

Installation

Prerequisites

Setup

Architecture

Available Tools (10 Total)

Content Processing (5 tools)

1. process_document

2. process_uploaded_document

3. process_text

4. process_url

5. process_batch

Q&A System (1 tool)

6. ask_question

Document Management (3 tools)

7. list_documents

8. delete_document

9. clear_all_documents

Utility Tools (1 tool)

10. get_status

Common Workflows

File Upload (Claude Desktop)

Single Document Q&A (Any MCP Client)

Multi-Document Research (Any MCP Client)

Configuration

Environment Variables (.env)

Model Selection

Storage

API Costs

Troubleshooting

Server won't start

Import errors

API Key errors

Empty knowledge base after restart

File upload not working

Development

Running Tests

Debugging

Project Structure

MCP Client Compatibility

Tested Clients

Compatibility Notes

License

Support

Reviews

1. `process_document`

2. `process_uploaded_document`

3. `process_text`

4. `process_url`

5. `process_batch`

6. `ask_question`

7. `list_documents`

8. `delete_document`

9. `clear_all_documents`

10. `get_status`