A three-phase program for accurate identification of context
For cases where tool recommendations are out of alignment in multilingual mashups or special framework projects, the following process can be optimized:
preanalytical stage
- Create a file in the project root directory declaring the project type (e.g. FullStack/DataScience)
- Perform a depth scan by bringing up the command panel
run-time adjustment
- When a recommendation discrepancy occurs, right-click on the plugin icon and select the
- Manual labeling of code block types (front-end/back-end/tests, etc.)
- Use the function to compare the effects of different tools (generates temporary logs)
Long-term improvements
- Enabled in -, the plugin will improve the algorithm based on the user's choice of habits
- Participation program to add identification rules for special frames
- Enterprise users can train private recognition models and import them into
Typical application scenarios:
- Recognizing both JSX and native components in React Native projects
- Mixing Markdown and Python Code Blocks within Jupyter Notebook
This answer comes from the articleMCP Jetpack: an automated MCP plugin for fast connection to AI toolsThe
May not be reproduced without permission:AI productivity tools " How to overcome inaccurate AI tool recommendations in complex project environments?
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