Which integrations are available for this server?

Provides tools for accessing [GitHub](/mcp/servers/integrations/github) repositories, commits, files, and issues, including listing repos, fetching file content, and managing issues programmatically. Allows reading and summarizing emails through OAuth authentication with read-only access to [Gmail](/mcp/servers/integrations/gmail) messages. Provides access to [Steam](/mcp/servers/integrations/steam) gaming data, including listing recently owned games and viewing playtime statistics. Enables access to [YouTube](/mcp/servers/integrations/youtube) data via OAuth, including listing liked videos, liked songs from YouTube Music, and retrieving playlist information.

AI Personal Hub

Unified personal assistant that connects Local Files, GitHub, YouTube, Gmail, Steam, and Summarization through Model Context Protocol (MCP), with a clean Flask UI and LM Studio integration.

Features

Local notes: list and open .txt files from notes/
GitHub: repos, commits, list files, fetch file content, issues
YouTube: Liked Videos (LL), Liked Songs (LM) via OAuth
Gmail: read last emails via OAuth
Steam: recent owned games and playtime
Summarizer: prompt exposed as an MCP tool
Modern chat-style UI with quick actions and linkified results

Architecture

Flask serves the UI and a simple /ask endpoint
LM Studio runs a local OpenAI-compatible server for LLM responses
MCP server (mcp_server/server.py) exposes tools that LM Studio can call

Browser ↔ Flask UI ↔ LM Studio (LLM) ↔ MCP Tools (python -m mcp_server.server)

Setup

Python env

python -m venv .venv .\.venv\Scripts\Activate.ps1 pip install -r requirements.txt

LM Studio (recommended defaults)

Start LM Studio local server (OpenAI-compatible) on http://localhost:1234
Set environment variables (PowerShell):

setx LM_STUDIO_BASE_URL http://localhost:1234 setx LM_STUDIO_API_KEY lm-studio setx LM_STUDIO_MODEL your-model-name

MCP server registration in LM Studio

Edit LM Studio mcp.json and add:

{ "mcpServers": { "personal-hub-server": { "command": "D:\\AI_MCP\\ai-personal-hub\\.venv\\Scripts\\python.exe", "args": ["-m", "mcp_server.server"], "cwd": "D:\\AI_MCP\\ai-personal-hub" } } }

Then start the server in LM Studio Tools (MCP), or run manually:

python -m mcp_server.server

Service credentials

GitHub: set GITHUB_TOKEN (PAT, repo read scope recommended)
Steam: set STEAM_API_KEY and STEAM_ID
Notes: create notes/ with .txt files

YouTube OAuth (token.json)

In Google Cloud: enable "YouTube Data API v3"
Create OAuth client (Desktop app) → download client_secret.json to project root
Generate token.json (one-time):

python -c "from google_auth_oauthlib.flow import InstalledAppFlow; import json; flow=InstalledAppFlow.from_client_secrets_file('client_secret.json',['https://www.googleapis.com/auth/youtube.readonly']); creds=flow.run_local_server(port=0); open('token.json','w',encoding='utf-8').write(creds.to_json())"

Gmail OAuth (token.json)

Similar flow; ensure scope https://www.googleapis.com/auth/gmail.readonly

Run

Flask UI:

python app.py

Open http://127.0.0.1:5000/

MCP server (separate terminal):

python -m mcp_server.server

Available MCP tools

Files: list_local_files, fetch_local_file
GitHub: github_repos, github_commits, github_list_files, github_file_content, github_issues, github_issue
YouTube: yt_liked_videos, ytm_liked_songs, yt_playlist
Gmail: read_emails
Steam: steam_games
Summarize: summarize prompt

Example prompts

“List my local notes.”
“Open a.txt.”
“List repos for Harsh-1807.”
“List files in Harsh-1807/weather.”
“Open README.md from Harsh-1807/weather.”
“Show open issues for Harsh-1807/weather.”
“List 5 of my liked YouTube videos.”
“List 5 of my liked songs on YouTube Music.”
“Summarize my last 5 emails.”
“Which Steam games do I play most?”

Troubleshooting

LLM answers without calling tools: lower temperature; add a system prompt telling it to prefer MCP tools; ensure the tool server is running and registered in LM Studio.
YouTube LM liked songs not returning results: some accounts do not expose LM over the API; try yt_liked_videos (LL) or yt_playlist("LL").
Permission errors: re-create token.json for the correct Google account; verify scopes.