$ loading_
Automatically detects WBSO-relevant work and logs R&D time entries.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "WBSO MCP Server" yet — see the docs or source repo.
Based on my work log today, identify which activities are WBSO-relevant and create time tracking entries using the project criteria: fixed an algorithm bug for 2 hours in the morning, implemented a prototype API for 3 hours in the afternoon, and attended a requirements meeting for 1 hour in the evening.
Returns identified qualifying R&D activities and draft time tracking entries.
Using the configured project criteria, check whether the following work should be logged as WBSO R&D time and provide logging recommendations: writing test scripts, organizing meeting notes, designing an experiment plan, and deploying a demo environment.
Provides a decision for each task and indicates which ones should be logged as R&D time.
Create WBSO-related time tracking entries from this daily summary: today I mainly validated a new model approach, recorded experiment results, and discussed the next technical implementation steps with the team.
Generates structured time entry content ready for later submission or filing.
Developers or researchers can have AI identify WBSO-relevant R&D activities from daily work using project rules and automatically create time entries, reducing manual tracking effort.
When a team needs to track R&D time consistently, this tool can filter and log work based on configurable project criteria to keep records aligned.
It enables AI assistants to automatically detect WBSO-relevant work and log R&D time tracking entries based on configurable project criteria.
From the description, you at least need configurable project criteria so the AI can determine relevant work and create time entries. For other installation or runtime prerequisites, see the source repository.
It does more than simple time logging: it first detects whether work is WBSO-relevant, then creates R&D time entries based on project rules. For further differences, see the source repository.
Track time, assign projects, and manage daily schedules with natural language.
Create and edit WPS documents, spreadsheets, and presentations using natural language.
Track time, manage projects, and export invoices using natural language.
Extract and analyze Time Doctor tracking data through AI assistants.
Analyze, manage, and debug DBOS workflows and authentication issues.
Generate SBOMs, scan CVEs, and check license compliance in VS Code.