Cal2Prompt – Turning Calendar Data into AI‑Ready Prompts

Cal2Prompt solves a common pain point for developers building conversational agents: how to inject real‑time scheduling information into an LLM’s context without manual copy‑and‑paste. By pulling events directly from Google Calendar and rendering them into a single, templated prompt, the tool bridges the gap between external data sources and AI‑driven workflows. This eliminates repetitive manual updates, ensures up‑to‑date context for the model, and keeps sensitive data out of the prompt payload.

What Cal2Prompt Does

At its core, Cal2Prompt is a lightweight command‑line utility that authenticates via OAuth 2.0 with Google Calendar API v3, retrieves events for a user‑specified window (today, this week, next month, etc.), and then passes that data through a Jinja2 template engine. The output can be printed to stdout or served as an experimental Model Context Protocol (MCP) server. When used as an MCP server, any LLM client that understands the protocol can request a fresh schedule prompt on demand, allowing dynamic context injection during a conversation.

Key Features & Capabilities

• Google Calendar Integration – Seamless access to the user’s calendar with OAuth2 authentication and secure token storage.
• Template‑Driven Prompt Generation – Jinja2 templates let developers control the exact wording, formatting, and metadata included in the prompt.
• Fast Rust‑Backed Engine – The underlying implementation is written in Rust, delivering low latency even for large event lists.
• MCP Server Mode – Exposes a simple, stateless endpoint that LLM assistants can call to fetch the latest prompt without needing direct API access.
• Flexible Date Ranges – Built‑in flags (, , etc.) simplify common scheduling queries.

Use Cases & Real‑World Scenarios

• Virtual Assistants – A chatbot can answer “What’s on my agenda tomorrow?” by querying Cal2Prompt in real time, ensuring the response reflects the latest calendar changes.
• Meeting Summaries – An LLM can generate a concise briefing for the day by ingesting the schedule prompt before drafting an email or slide deck.
• Personal Productivity Tools – Developers can build voice‑activated routines that first pull the schedule, then prompt an AI to suggest optimal task blocks or reminders.
• Contextual Chat Applications – Web or mobile chat interfaces can fetch the user’s calendar context on each new session, keeping conversations relevant without storing sensitive data locally.

Integration into AI Workflows

Because Cal2Prompt adheres to MCP, it plugs cleanly into any architecture that supports the protocol. An assistant can issue a simple request to and receive a ready‑to‑use prompt string. This decouples the calendar service from the LLM, allowing developers to swap out data sources or update templates without touching the model logic. The server can also run in a containerized environment, scaling horizontally to serve multiple users concurrently.

Unique Advantages

• Zero‑Code Prompt Generation – No need to write custom serializers; Jinja2 handles all formatting.
• Security by Design – OAuth tokens are stored locally and never exposed to the LLM, keeping credentials out of model context.
• Experimental MCP Support – Early adopters can experiment with protocol‑based data fetching, paving the way for more sophisticated context pipelines.
• Rust Performance – Even though the user interface is a CLI, the core logic runs at native speed, ensuring minimal latency for real‑time applications.

Cal2Prompt offers a streamlined, secure, and extensible bridge between Google Calendar data and AI assistants, making it an essential tool for developers who need up‑to‑date scheduling context in their conversational applications.