Google Calendar-Sheet Assistant — Full Edition (MCP + LLM)

Google Calendar-Sheet Assistant — Full Edition (MCP + LLM)

A complete Google Calendar assistant. Not just "create a meeting" — everycommon calendar task, exposed as an MCP tool, chosen automatically by an LLMbased on what you type.

        User
         │
         ▼
"Move my 3 PM meeting to 5 PM"
         │
         ▼
   OpenAI / GPT-OSS-120B
         │
(Decides which tool(s) to call — can chain more than one)
         │
         ▼
   MCP Client (Python)          ← client.py
         │
 Calls tool(s) through MCP protocol
         │
         ▼
Google Calendar MCP Server       ← server.py  (14 tools)
         │
 Executes Google Calendar API    ← calendar_utils.py
         │
         ▼
     Google Calendar
         │
         ▼
Success / Event Details
         │
         ▼
        User

All supported tasks

Project structure

google-calendar-sheet-mcp-/
├── server.py            # MCP server — 15 Calendar tools + 8 Sheets tools
├── client.py            # MCP client — LLM picks tool(s), can chain multiple calls
├── calendar_utils.py     # All Google Calendar API logic + OAuth (token.json)
├── sheets_utils.py        # All Google Sheets API logic + OAuth (token_sheets.json)
├── requirements.txt
├── .env.example
└── README.md

Setup

1. Google Cloud (one-time)

1. Google Cloud Console → create/select a project.
2. APIs & Services → Library → enable Google Calendar API AND Google Sheets API (search + enable both, same project).
3. OAuth consent screen → External → add your email as a test user.
4. Credentials → Create Credentials → OAuth client ID → Desktop app.
5. Download JSON → rename to credentials.json → place next to server.py. (This one file is reused by both calendar_utils.py and sheets_utils.py.)

2. Install

cd AI-AGENT
python3 -m venv venv
source venv/bin/activate        # Windows: venv\Scripts\activate
pip install -r requirements.txt

3. Configure

cp .env.example .env
# add GROQ_API_KEY (https://console.groq.com/keys)

4. Run

python3 client.py

First run opens a browser to authorize Calendar access (saves token.json).The first time you ask it to do anything with Sheets, a secondbrowser prompt appears — authorizing Sheets access separately (savestoken_sheets.json). This is expected: Calendar and Sheets aredifferent permissions, so they get separate consent + separate tokenfiles, even though both use the same credentials.json app identity.

Google Sheets — setup notes & example prompts

Every Sheets tool needs a spreadsheet_id — the long string in asheet's URL, between /d/ and /edit:

https://docs.google.com/spreadsheets/d/1AbCdEfGhIjKlMnOpQrStUvWxYz/edit
                                        └──────── this part ────────┘

Example prompts:

• "Create a new spreadsheet called 'Q3 Leads'." → create_spreadsheet
• "In spreadsheet [id], what's in Sheet1 rows 1 to 10?" → read_sheet
• "Add a row to spreadsheet [id]: Priya, [email protected], Contacted" → append_sheet_row
• "Overwrite A1:B2 in [id] with these values..." → write_sheet
• "What tabs does spreadsheet [id] have?" → list_sheet_tabs
• "Add a new tab called 'August' to [id]." → add_sheet_tab
• "Clear rows 2 to 50 in Sheet1 of [id]." → clear_sheet_range
• "Give me the title and link for spreadsheet [id]." → get_spreadsheet_info

Sharing note: the Google account you authorized with (whicheverone created token_sheets.json) needs edit access to any spreadsheetyou ask it to read/write — either it owns the sheet, or someone sharedit with that account.

How multi-step requests work

Some tasks need more than one tool call — e.g. "Move my 3 PM meeting to 5 PM"requires first finding the event (no id was given), then updating it.client.py handles this with a loop: it keeps letting the LLM call toolsback-to-back (find → then act) until the LLM has enough information to giveyou a final plain-language answer. You'll see each intermediate tool callprinted, e.g.:

You: Move my 3 PM meeting to 5 PM
[client] LLM chose tool: search_events({'query': '3 PM'})
[client] LLM chose tool: update_event({'event_id': 'abc123', 'start_time': '...', 'end_time': '...'})

Assistant: Done — moved your meeting to 5:00–5:30 PM today.

Recurring events — how RRULE works

schedule_event's recurrence argument takes standard iCalendar RRULEstrings. The LLM constructs these automatically, but for reference:

• Every Monday: RRULE:FREQ=WEEKLY;BYDAY=MO
• Every weekday: RRULE:FREQ=WEEKLY;BYDAY=MO,TU,WE,TH,FR
• Every day for 10 occurrences: RRULE:FREQ=DAILY;COUNT=10
• Every month on the 1st: RRULE:FREQ=MONTHLY;BYMONTHDAY=1

Watch calendar changes — important note

watch_calendar sets up Google push notifications, but Google will onlysend them to a public HTTPS URL you control — not localhost. Forlocal development:

1. Run a tiny webhook receiver (Flask/FastAPI) that logs/handles the POSTGoogle sends on changes.
2. Expose it publicly with a tunnel tool (e.g. ngrok http 8000).
3. Call watch_calendar with that public https://...ngrok.../webhook URL.
4. The subscription expires (Google enforces a max TTL, typically up to~7 days) — re-run watch_calendar periodically (e.g. a daily cron job)to keep it alive.

This part is the most "production infrastructure"-heavy feature here — theother 13 tools work immediately with no extra hosting required.

Testing the server alone (no LLM)

npx @modelcontextprotocol/inspector python3 server.py

Lets you call any of the 14 tools directly from a browser UI to confirm theCalendar integration works before wiring up chat.

Troubleshooting