IMAP MCP Server

# AI-Assisted Development Workflow This document outlines the workflow for using AI assistants (like Claude Sonnet) with GitHub Issues to autonomously develop features for the IMAP MCP project. ## Overview The integration of AI assistants with GitHub Issues provides an autonomous development system that: - Prioritizes work through GitHub Issues - Maintains consistent development patterns - Enforces quality through automated testing - Preserves institutional knowledge in issue history - Reduces manual overhead in development ## Autonomous Feature Development Process ### 1. Task Planning & Creation AI assistants can participate in task planning by: - Analyzing existing codebase and identifying improvement opportunities - Creating issues with appropriate priority and status labels: ```bash gh issue create --title "New Feature Name" --body "Detailed description..." --label "priority:X" --label "status:prioritized" ``` - Breaking down complex tasks into smaller, manageable sub-issues - Linking related issues together with references ### 2. Feature Implementation When starting work on an issue: ```bash # Check current status python scripts/issue_helper.py check <issue-number> # Start work on the issue (creates branch, updates status) python scripts/issue_helper.py start <issue-number> ``` This workflow automatically: - Creates a feature branch with appropriate naming - Updates issue status to `in-progress` - Links commits to the issue for traceability ### 3. Test-Driven Development AI assistants should follow TDD practices: 1. Write tests that cover the expected functionality 2. Run tests to verify they fail appropriately 3. Implement features to make tests pass 4. Refactor while maintaining test success ```bash # Run tests uv run pytest tests/ # Run with coverage uv run pytest --cov=imap_mcp ``` ### 4. Pull Request Creation When implementation is complete: ```bash # Complete the issue (creates PR, links issue) python scripts/issue_helper.py complete <issue-number> ``` This automatically: - Creates a pull request linked to the issue - Includes issue references in the PR description - Updates issue status to `in-review` ### 5. Code Integration Once PR checks pass and reviews are completed: ```bash # Merge the PR gh pr merge <pr-number> ``` This triggers workflows that: - Merge the changes into the main branch - Update issue status to `completed` - Reorganize priorities of remaining issues if appropriate ## Benefits for AI Development ### Reduced Context Window Usage By leveraging GitHub Issues instead of in-chat task lists: - Each feature can be developed with focused context - Issue details provide precise requirements without needing to remember all project details - Issue references create implicit links between related features ### Improved Output Consistency The standardized workflow ensures: - Uniform code style and patterns across features - Consistent test coverage and quality standards - Reliable commit message formats that trigger appropriate status transitions ### Enhanced Collaboration AI assistants can collaborate with human developers through: - Issue comments for progress updates and clarifications - PR reviews for feedback and improvements - Status transitions that keep everyone informed ## Automated Validation The status updater script ensures issues accurately reflect development status: ```bash python scripts/issue_status_updater.py --owner <repo-owner> --repo <repo-name> --issue <issue-number> ``` This script can be run: - Locally by developers or AI assistants - Automatically through GitHub Actions on commits and PR events - As part of a scheduled validation process ## Success Metrics Autonomous development success can be measured by: - Percentage of issues completed with minimal human intervention - Test coverage maintained across features - Frequency of status transitions indicating steady progress - Reduction in time from issue creation to completion ## Continuous Improvement Through the issue history, the system accumulates knowledge about: - Which development patterns lead to successful implementations - Common challenges and their solutions - Optimal task sizing for AI development - Patterns that could benefit from additional automation