Onboarding MCP Server

Onboarding MCP Server

A local-first MCP server that enables AI coding assistants to map codebases, transcribe onboarding videos with keyframe screenshots, and maintain structured Obsidian knowledge vaults directly from IDE chat.

Category
Visit Server

README

Onboarding MCP Server 🚀

A local-first Model Context Protocol (MCP) server that empowers AI coding assistants (e.g., Cursor, Claude Code, Antigravity IDE) to autonomously map codebases, transcribe onboarding videos with keyframe screenshots, and maintain structured knowledge vaults in Obsidian directly from IDE chat interactions.


🌟 Key Capabilities

  1. set_vault_path: Dynamically configure the active Obsidian vault directory for session state from IDE chat.
  2. set_workspace_path: Dynamically configure the target codebase workspace directory for session state from IDE chat.
  3. get_codebase_structure: Recursively map code directory structures and detect dependency manifests, respecting .gitignore (uses configured session workspace path or optional workspace_path parameter).
  4. process_video: Transcribe onboarding videos using faster-whisper (CPU int8), extract scene keyframes via OpenCV, and output timestamp-aligned transcript data.
  5. write_obsidian_note: Write structured Markdown notes and MOCs (Maps of Concepts) directly into the user's local Obsidian vault (uses configured session vault path or optional vault_path parameter).

🚀 Quickstart

Option 1: Docker (Recommended - Zero Host Setup)

Build and run the containerized server with pre-packaged ffmpeg and dependencies. Note: because Docker containers run in an isolated environment, volume mounts (-v) are required to grant the container access to host directories specified in chat:

# Build the Docker image
make build-docker

# Run container (mount host directories so chat can access them)
docker run -i --rm -v $HOME:$HOME onboarding-mcp-server

Option 2: Local Python Environment (No Volume Mounts Needed)

Runs directly on your host OS. Any absolute path passed from IDE chat works natively without volume mounts.

Requires Python 3.11+ and ffmpeg installed on host (brew install ffmpeg on macOS).

# Install dependencies into virtualenv
make setup

# Run the tests
make test

# Run the MCP server over stdio
.venv/bin/python -m onboarder.server

⚙️ IDE Chat Integration & Setup

The IDE chat assistant can configure workspace and vault paths dynamically at any point during a chat session:

  • set_workspace_path(path="/path/to/repo")
  • set_vault_path(path="/path/to/ObsidianVault")

Alternatively, individual tools (get_codebase_structure, write_obsidian_note, process_video) accept optional workspace_path and vault_path arguments directly.

Docker Configuration Example (claude_desktop_config.json / Cursor / Antigravity)

Mount your user home directory (or relevant code/vault folders) so paths passed in chat are accessible:

{
  "mcpServers": {
    "onboarding-mcp": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-v",
        "/Users/yourname:/Users/yourname",
        "onboarding-mcp-server"
      ]
    }
  }
}

Local Python Configuration Example

{
  "mcpServers": {
    "onboarding-mcp": {
      "command": "/absolute/path/to/onboarding-mcp/.venv/bin/python",
      "args": ["-m", "onboarder.server"]
    }
  }
}

🛠 Developer Workflow

make setup    # Set up venv and install dev dependencies
make test     # Run pytest suite
make lint     # Run ruff check and mypy type checking
make format   # Run ruff formatter

📄 License

MIT

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured