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Ryeliu

worklab-tools

by Ryeliu
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worklab-tools — MCP Server for Personal Knowledge Management

A Model Context Protocol (MCP) server that provides AI-powered tools for knowledge management, including semantic search, audio transcription, image generation, and PPT creation.

Built for use with Claude Code and the Knowledge system.

Tools

Tool

Description

search_knowledge(query, type?, company?, person?)

Semantic search over knowledge base (ChromaDB + bge-m3)

index_knowledge()

Full rebuild of vector index from all markdown files

upsert_knowledge()

Incremental index update (MD5 diff, only re-index changed files)

generate_ppt(topic, context_text?, page_count?)

Generate PPT: plan outline → slide images → PDF output

generate_image(prompt, filename?)

Generate image from text description (Gemini)

transcribe_audio(file_path, model_size?)

Speech-to-text with speaker diarization (Whisper + pyannote)

register_voiceprint(name, file_path)

Register speaker voiceprint for future identification

Related MCP server: rag-mcp

Setup

# Clone
git clone https://github.com/Ryeliu/worklab-tools.git
cd worklab-tools

# Install dependencies (requires Python 3.12+, uv recommended)
uv sync

# Configure environment
cp .env.example .env
# Edit .env with your API keys

# Run
uv run mcp run server.py

Environment Variables

Create a .env file:

GEMINI_API_KEY=your_google_ai_studio_key
HF_TOKEN=your_huggingface_token
OPENROUTER_BASE_URL=https://openrouter.ai/api/v1
OPENROUTER_API_KEY=your_openrouter_key

Variable

Required For

Source

GEMINI_API_KEY

generate_image

Google AI Studio

HF_TOKEN

transcribe_audio, register_voiceprint

HuggingFace

OPENROUTER_API_KEY

generate_ppt

OpenRouter

Register with Claude Code

Add to your Claude Code MCP settings:

{
  "mcpServers": {
    "worklab-tools": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/worklab-tools", "mcp", "run", "server.py"],
      "env": {
        "GEMINI_API_KEY": "...",
        "HF_TOKEN": "...",
        "OPENROUTER_BASE_URL": "https://openrouter.ai/api/v1",
        "OPENROUTER_API_KEY": "..."
      }
    }
  }
}

Dependencies

  • Semantic Search: chromadb, sentence-transformers (BAAI/bge-m3, CPU)

  • Transcription: openai-whisper (large-v3), pyannote.audio (speaker diarization)

  • PPT Generation: openai SDK (OpenRouter → Gemini), Pillow

  • Image Generation: httpx (Gemini API direct)

License

MIT

This server cannot be installed

F
license - not found
-
quality - not tested
D
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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