MyAIGist MCP Server

Local MCP server for Claude Desktop providing document intelligence and knowledge management. Process documents, answer questions with RAG, and transcribe media - all running locally with zero infrastructure costs.

Overview

MyAIGist MCP provides 11 powerful tools for document intelligence and knowledge management:

• Document Processing: PDF, DOCX, TXT, URLs, and batch processing

• Media Transcription: Audio and video transcription with Whisper

• Q&A System: RAG-powered question answering with voice support

• Knowledge Management: Persistent vector storage with document tracking

Features

✅ Core document intelligence - Process, summarize, and search documents ✅ Local execution - Runs in Claude Desktop ✅ Persistent storage - Single vector store across sessions ✅ Multi-document RAG - Unlimited documents (no 5-doc limit) ✅ Media transcription - Whisper-powered audio/video transcription ✅ Zero infrastructure costs - Replaces $200-400/month AWS deployment

Installation

Prerequisites

• Python 3.8+

• OpenAI API key

• Claude Desktop installed

Setup

1. Install dependencies:

   cd /Users/mikeschwimmer/myaigist_mcp pip install -r requirements.txt

2. Configure environment:

   cp .env.example .env # Edit .env and add your OPENAI_API_KEY

3. Configure Claude Desktop:

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

   { "mcpServers": { "myaigist": { "command": "/Library/Frameworks/Python.framework/Versions/3.13/bin/python3", "args": ["/Users/mikeschwimmer/myaigist_mcp/server.py"] } } }

4. Restart Claude Desktop

   The MCP server will start automatically when you open Claude Desktop.

Architecture

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

Available Tools (11 Total)

Content Processing (5 tools)

1. process_document

Process PDF, DOCX, or TXT files and add to knowledge base.

Parameters:

• file_path (string, required): Path to document file

• title (string, optional): Document title (defaults to filename)

• summary_level (string, optional): quick, standard, or detailed

Example:

2. process_text

Process raw text and add to knowledge base.

Example:

3. process_url

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

Example:

4. process_media

Transcribe audio or video file and add transcript to knowledge base.

Supported formats:

• Audio: MP3, WAV, FLAC, M4A, AAC, OGG, WMA

• Video: MP4, AVI, MOV, WMV, FLV, WebM, MKV, M4V

Example:

5. process_batch

Process multiple files and generate unified summary.

Example:

Q&A System (2 tools)

6. ask_question

Ask questions about stored documents using RAG.

Example:

7. ask_question_voice

Transcribe voice question and answer using RAG.

Example:

Document Management (3 tools)

8. list_documents

List all documents in knowledge base with metadata.

Example:

9. delete_document

Delete specific document by ID.

Example:

10. clear_all_documents

Clear entire knowledge base.

Example:

Utility Tools (1 tool)

11. get_status

Get system status and knowledge base statistics.

Example:

Common Workflows

Single Document Q&A

Multi-Document Research

Media Transcription

Configuration

Environment Variables (.env)

Model Selection

Recommended for best quality:

• gpt-4o-mini - Best balance of quality and cost

• text-embedding-3-large - Higher accuracy for RAG

• whisper-1 - Current transcription model

Storage

Vector Store:

• Path: data/vector_store.pkl

• Format: Pickle with numpy arrays

• Persistence: Survives server restarts

• Capacity: Unlimited documents

Temporary Files:

• Path: audio/ directory

• Used for: Whisper transcription temporary files

• Cleanup: Automatic by system

Cost Savings

Before (AWS/Flask): $200-400/month

• ECS Fargate compute

• Load balancer

• CloudWatch

• Data transfer

After (MCP/Local): $0/month infrastructure

• Runs locally on your machine

• Only OpenAI API costs (usage-based)

OpenAI API Costs (estimated monthly):

• 100 documents processed: ~$5-10

• 500 questions answered: ~$2-5

• 10 hours media transcribed: ~$4

• Total: ~$10-20/month (vs $200-400 AWS)

Troubleshooting

Server won't start

Import errors

Empty knowledge base after restart

• Check data/vector_store.pkl exists

• Verify file permissions (readable/writable)

• Check for errors in server logs

Development

Running Tests

Debugging

Project Structure

Related Projects

This project is self-contained with no external dependencies beyond Python packages.

Recent Changes

2026-01-19: Removed audio generation functionality

• Focused on core document intelligence features

• Removed TTS audio generation tools

• Kept Whisper transcription for media files

• Simplified from 13 to 11 tools

• See AUDIO_REMOVAL_SUMMARY.md for details

License

Same as original myaigist project.

Support

For issues or questions:

1. Check troubleshooting section above

2. Review MCP logs: ~/Library/Logs/Claude/mcp-server-myaigist.log

3. Verify environment variables in .env

4. Test agent imports individually

Last Updated: 2026-01-19 Project Status: ✅ Complete - 11 core tools implemented and tested Cost Savings: $200-400/month (AWS → $0) Focus: Document intelligence without audio complexity