passage-of-time-mcp
MCP ServerFree🤖🕰️ An MCP server that gives language models temporal awareness and time calculation abilities. Teaching AI the significance of the passage of time through collaborative tool development.
Capabilities8 decomposed
timezone-aware current datetime retrieval with contextual metadata
Medium confidenceExposes the current date and time with full timezone support through the MCP protocol, returning both ISO 8601 timestamps and human-readable formats. Implements timezone-aware datetime calculations using Python's pytz library integrated into the FastMCP framework, allowing LLMs to query the server for the precise current moment in any specified timezone without relying on training data cutoffs or hallucinated timestamps.
Designed specifically for LLM temporal reasoning rather than general-purpose time APIs — returns both machine-readable ISO 8601 and human-contextual information (e.g., business hours, weekend status) in a single call, addressing the architectural gap where LLMs lack real-time temporal grounding
Unlike generic system time APIs or web services, this tool is optimized for LLM consumption with human-contextual metadata built-in, eliminating the need for LLMs to perform secondary reasoning about what the current time means
multi-format timestamp parsing and normalization
Medium confidenceConverts arbitrary timestamp formats (Unix epoch, ISO 8601, RFC 2822, human-readable strings) into normalized datetime objects with timezone awareness. Implements a format-detection pipeline using Python's dateutil.parser combined with regex-based heuristics to identify and parse ambiguous timestamp strings, exposing the parsed result through MCP with validation and error reporting for malformed inputs.
Combines dateutil's fuzzy parsing with format-detection heuristics to handle the ambiguity that LLMs encounter when processing real-world temporal data, returning both the parsed result and metadata about which format was detected — enabling LLMs to reason about timestamp reliability
More flexible than strict format validators and more reliable than LLM-native parsing, which frequently hallucinates timestamps; provides confidence scores and format detection that help LLMs understand parsing uncertainty
duration calculation and temporal difference computation
Medium confidenceCalculates the elapsed time between two timestamps or from a timestamp to the present, returning durations in multiple human-readable formats (days, hours, minutes, seconds, and natural language descriptions). Implements timezone-aware datetime subtraction using Python's datetime module with support for DST transitions, exposing results through MCP with both machine-readable duration objects and human-contextual descriptions like 'about 2 weeks' or 'less than a minute'.
Specifically designed for LLM temporal reasoning by returning both precise numerical durations and human-contextual descriptions in a single call, eliminating the need for LLMs to perform secondary formatting or interpretation of raw time differences
Unlike generic time libraries that return raw seconds or timedelta objects, this tool provides LLM-optimized output with natural language descriptions and relative time phrases that LLMs can directly use in responses without additional processing
temporal arithmetic with add/subtract operations
Medium confidenceAdds or subtracts time intervals (days, hours, minutes, seconds) from a given timestamp, returning the resulting datetime with full timezone awareness and DST handling. Implements interval arithmetic using Python's timedelta objects combined with pytz timezone handling, allowing LLMs to perform forward and backward temporal projections for scheduling, deadline calculation, and temporal reasoning without manual arithmetic.
Provides timezone-aware interval arithmetic specifically for LLM use cases, handling DST transitions automatically and returning both the computed datetime and human-readable format in a single call — eliminating the need for LLMs to reason about timezone edge cases
More reliable than LLM-native date arithmetic (which frequently produces off-by-one errors) and more LLM-friendly than raw timedelta objects, with automatic DST handling that generic time libraries require manual configuration for
human-contextual temporal metadata extraction
Medium confidenceAnalyzes a timestamp and returns contextual information about when that moment falls in human terms: whether it's a weekday or weekend, business hours or after-hours, morning/afternoon/evening, and other human-centric temporal categories. Implements context detection using configurable business hour definitions and calendar logic, exposing results through MCP as structured metadata that helps LLMs reason about temporal significance beyond raw timestamps.
Designed from collaborative human-AI development to provide the specific contextual dimensions that LLMs need for temporal reasoning — business hours, weekday/weekend, time of day — rather than raw timestamp data, addressing the architectural gap where LLMs lack intuitive understanding of temporal significance
Unlike generic datetime libraries that return only raw date/time components, this tool provides LLM-optimized contextual metadata that enables more human-aware temporal reasoning without requiring LLMs to implement business logic themselves
duration formatting and human-readable time representation
Medium confidenceConverts raw duration values (seconds, milliseconds, or timedelta objects) into multiple human-readable formats: natural language descriptions ('about 2 weeks'), abbreviated formats ('2w 3d'), and detailed breakdowns (days/hours/minutes/seconds). Implements format selection logic that chooses the most appropriate representation based on duration magnitude, exposing results through MCP with both machine-readable and human-contextual outputs for LLM consumption.
Provides LLM-optimized duration formatting that returns multiple representation styles in a single call, allowing LLMs to choose the most appropriate format for their output context without requiring secondary formatting logic
More flexible than fixed-format duration libraries and more LLM-friendly than raw timedelta objects, with automatic format selection that adapts to duration magnitude and context
mcp protocol tool registration and fastmcp framework integration
Medium confidenceRegisters all temporal tools as callable MCP endpoints through the FastMCP framework, managing tool schema definition, input validation, and protocol-level communication with MCP clients. Implements a single global FastMCP instance that handles tool discovery, parameter marshalling, and response serialization, enabling seamless integration with Claude and other LLM applications that support the Model Context Protocol without requiring manual API configuration.
Leverages FastMCP's declarative tool registration pattern to expose temporal capabilities as first-class MCP tools with automatic schema generation and protocol handling, eliminating manual API configuration and enabling direct LLM integration without middleware
Simpler and more maintainable than custom MCP server implementations, with automatic schema generation and protocol compliance built-in; more direct than REST API wrappers, with lower latency and tighter LLM integration
timezone database management and dst-aware calculations
Medium confidenceManages timezone information using the pytz library with automatic Daylight Saving Time (DST) transition handling across all temporal calculations. Implements timezone-aware datetime arithmetic that accounts for DST boundaries, ensuring that operations like adding days or calculating durations across DST transitions produce correct results without manual offset adjustments. Exposes timezone validation and DST status information through MCP for LLM awareness of temporal edge cases.
Provides LLM-aware DST handling that automatically accounts for timezone transitions in all temporal calculations, eliminating the need for LLMs to manually reason about offset changes or DST edge cases — a common source of temporal errors in LLM-generated code
More reliable than LLM-native timezone arithmetic (which frequently produces off-by-one-hour errors across DST boundaries) and more transparent than opaque timezone libraries, with explicit DST status information that helps LLMs understand temporal uncertainty
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓LLM agents building scheduling systems
- ✓AI assistants handling multi-timezone coordination
- ✓Developers integrating temporal awareness into Claude or other LLM applications
- ✓LLM agents processing logs or historical data with mixed timestamp formats
- ✓AI systems ingesting data from multiple APIs with different timestamp conventions
- ✓Developers building temporal data pipelines that need format-agnostic parsing
- ✓LLM agents building monitoring or observability systems
- ✓AI assistants generating human-friendly temporal summaries
Known Limitations
- ⚠Depends on server system clock accuracy — no NTP synchronization built-in
- ⚠Timezone database updates require pytz library updates
- ⚠Returns server-local time if timezone parameter is invalid or omitted
- ⚠Ambiguous dates (e.g., '01/02/2024') may be parsed incorrectly depending on locale assumptions
- ⚠Very informal or colloquial time expressions ('next Tuesday', 'in a jiffy') are not supported
- ⚠Parsing performance degrades with extremely long or malformed input strings
Requirements
Input / Output
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Repository Details
Last commit: Jun 17, 2025
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🤖🕰️ An MCP server that gives language models temporal awareness and time calculation abilities. Teaching AI the significance of the passage of time through collaborative tool development.
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