feat: standardize on ISO 8601 for dates, add utility functions, and document design decisions.

docs/DESIGN_DECISIONS.md

@@ -0,0 +1,35 @@
+# Design Decisions - Strava MCP Server
+
+This document records key architectural and design decisions made during the development of the Strava MCP Server. It serves as a guide for AI agents and developers to maintain consistency.
+
+## 1. Date and Time Handling
+**Decision**: Standardize on ISO 8601 (UTC) for all internal data exchange, tool inputs, and tool outputs.
+**Date**: 2026-05-12
+**Context**: LLMs often struggle with ambiguous date formats (e.g., DD.MM.YYYY vs. MM/DD/YYYY). International users require a unified format.
+**Implementation**:
+- All tools accept ISO 8601 strings (`YYYY-MM-DDTHH:MM:SSZ`) for date parameters.
+- Tool outputs include a `_date` or similar field with the raw ISO 8601 string.
+- A shared utility `src/strava_mcp_server/utils.py` handles parsing and formatting.
+- **Human Readability**: While raw data is ISO 8601, markdown summaries presented to the user should use `DD.MM.YYYY HH:MM` for comfort, but the raw data for agent analysis must be standardized.
+
+## 2. Authentication & Token Management
+**Decision**: Automate token rotation and prioritize environment-based configuration.
+**Context**: Strava tokens expire every 6 hours. Manual refresh is tedious for automated use.
+**Implementation**:
+- The server checks token expiration before every request.
+- Tokens are automatically refreshed and updated in the environment/memory.
+- Initial authentication is handled via a separate `auth` script or integrated OAuth flow.
+
+## 3. Data Representation & MCP Annotations
+**Decision**: Use dual-content outputs with audience-specific annotations.
+**Context**: To provide a clean user experience while giving the LLM detailed data, we split tool results into two parts.
+**Implementation**:
+- **User Facing**: Markdown summaries annotated with `audience=["user"]`.
+- **Assistant Facing**: Raw JSON data annotated with `audience=["assistant"]`.
+- This allows MCP clients to intelligently decide what to display to the user while ensuring the assistant receives full technical context.
+
+## 4. Internationalization
+**Decision**: Use metric units (meters, km, m/s) internally and provide clear conversion instructions to the LLM.
+**Implementation**:
+- Strava API returns metric units. 
+- The LLM is instructed in `main.py` to convert these to human-friendly formats (km, km/h) based on user preference.