Background and core programs
Language models (e.g., ChatGPT) often exhibit 'time aphasia' due to the lack of a built-in clock system - unable to accurately compute date differences, perceive time zones, or understand temporal information.Passage of Time MCP does this by establishing a standardized interfaces to inject the time dimension of the physical world into AI conversational systems.
Specific implementation steps
- Function Call Integration: By
current_datetime()function to get an ISO 8601 format timestamp with a time zone parameter that supports the IANA standard (e.g.Asia/Tokyo) - Expansion of computing power: Use of
getTimeDifferenceMethods to handle time difference operations, built-in seconds/minutes/hours/days multi-granularity calculations with milliseconds error control - Real-time synchronization mechanism: Server-Sent Events technology for streaming time data with a minimum delay of 1 second for refresh.
Deployment optimization recommendations
Docker containerized deployment is recommended for production environments, via thedocker-compose.ymlConfigure time zone volume and NTP service synchronization. For time-sensitive scenarios, enable Redis to cache the results of the last 10 calculations to reduce the overhead of repeated calculations.
This answer comes from the articlePassage of Time MCP: A Service Providing Time-Awareness and Computational Capabilities for Language ModelingThe
