DateTime Tools for Langflow

Overview

The DateTime Tools MCP server offers a lightweight, timezone‑aware datetime utility for AI assistants that need to perform time calculations or display localized timestamps. By exposing simple methods such as get_current_datetime and get_week_number, the server eliminates the need for assistants to embed complex datetime logic or external libraries, streamlining the development of time‑dependent workflows.

This MCP solves a common pain point for developers building AI agents: reliably handling global time zones. Many existing solutions rely on hard‑coded offsets or assume UTC, leading to incorrect scheduling or data misinterpretation. The server integrates the IANA Time Zone Database via pytz, ensuring that every supported region— from “America/New_York” to “Asia/Tokyo”—is handled accurately. It also provides graceful error handling: invalid time zone identifiers trigger clear messages or sentinel values, preventing downstream failures in the assistant’s logic.

Key capabilities include:

• Current datetime retrieval with full ISO‑8601 formatting and offset information, enabling agents to timestamp actions or log events in the correct local time.
• Week number calculation that respects locale‑specific week start rules, useful for reporting or scheduling tasks that depend on calendar weeks.
• Comprehensive timezone coverage through the IANA database, covering all standard regions worldwide without additional configuration.
• Robust error handling that returns descriptive errors or fallback values, allowing assistants to implement retry logic or user prompts.

Real‑world use cases span a wide range of AI applications. A customer support agent can generate localized timestamps for ticket creation, while an automated scheduling assistant can compute the correct week number to align with weekly reporting cycles. In data pipelines, an AI orchestrator can tag logs with accurate local times before archiving them to a global storage system. For multilingual chatbots, the server ensures that time references match the user’s locale, improving conversational naturalness.

Integration into AI workflows is straightforward: developers expose the MCP as a tool within their Langflow or other orchestration platform, then invoke its methods from prompt templates or action nodes. The server’s simple interface—two primary functions and clear error signals—makes it a drop‑in replacement for any custom datetime logic, freeing developers to focus on higher‑level reasoning rather than time zone intricacies.