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Update MCP Server PrimitiveRequest updates to existing tools or resources in the CodeQL Development MCP Serverenhancement,mcp-server,update

📝 Created from issue template: .github/ISSUE_TEMPLATE/mcp-server-primitive-update.yml

Thanks for taking the time to improve existing MCP server primitives!

This template helps you request updates to existing tools and resources in the Model Context Protocol server. Updates can include bug fixes, feature enhancements, performance improvements, or expanded functionality.

🤖 For Agentic LLMs (GitHub Copilot)

This issue template provides structured information for automated primitive updates. When processing this issue:

  1. Repository Context: Begin by reading these critical reference files:

    • Repository Structure: README.md - Overview of MCP server architecture
    • Server Implementation: server/src/codeql-development-mcp-server.ts - Main server implementation
    • Current Tools: server/src/tools/codeql-tools.ts - Tool implementations to update
    • Current Resources: server/src/tools/codeql-resources.ts - Resource implementations to update
  2. Existing Primitive Analysis: Locate and analyze the target primitive:

    • Identify Location: Find the primitive implementation in the codebase
    • Current Behavior: Understand existing functionality and limitations
    • Dependencies: Check for related components and dependencies
  3. Update Requirements: Based on the issue description and examples:

    • Preserve Compatibility: Maintain existing API contracts where possible
    • Enhance Functionality: Add requested features or improvements
    • Fix Issues: Address any bugs or performance problems
  4. Validation Requirements:

    • All existing tests continue to pass
    • New functionality includes comprehensive tests
    • TypeScript compilation remains error-free
    • MCP protocol compliance maintained
  5. PR Workflow:

    • Create PR using .github/PULL_REQUEST_TEMPLATE/mcp-server-primitive-update.md
    • Include before/after comparisons and test coverage

What type of primitive are you updating?

Name of the existing tool or resource to update

What type of update are you requesting?

Describe how the primitive currently works and what issues exist

Describe how you want the primitive to work after the update

📋 Supporting Materials (Provide any/all that are available)

The following materials help define the desired updates and validate the improvements.

Example inputs that demonstrate the current limitations or new use cases

What the primitive should return after the update

Specific scenarios that should be tested to validate the update

Documentation that shows the desired functionality or standards to follow

How this update improves real AI assistant workflows

🐛 Issue Details (If this is a bug fix)

Steps to reproduce the current issue

Any error messages or logs that demonstrate the issue

🔗 Additional Context (Optional)

Links to related issues, documentation, or examples

How urgent is this update?

Are breaking changes to the API acceptable for this update?

🧪 Update Requirements (For LLM Implementation)

The following requirements must be met for primitive updates:

Update Checklist (Auto-populated for LLM guidance)

These requirements will be automatically validated during implementation

🔄 Update Workflow (For LLM Processing)

Automated Update Steps:

  1. Locate Primitive: Find the existing implementation in the codebase
  2. Analyze Current State: Understand current functionality and limitations
  3. Design Updates: Plan changes while preserving compatibility
  4. Implement Changes: Update the primitive with new functionality
  5. Update Tests: Modify and add tests to cover new behavior
  6. Validate Integration: Ensure compatibility with existing systems
  7. Update Documentation: Revise JSDoc and examples
  8. Performance Testing: Verify no regressions

Quality Gates:

  • ✅ All existing tests pass
  • ✅ New functionality thoroughly tested
  • ✅ TypeScript compilation clean
  • ✅ No performance regressions
  • ✅ API compatibility maintained (unless breaking changes approved)
  • ✅ Documentation updated

PR Decision Logic:

  • Ready for Review: All tests pass, documentation complete
  • Draft PR: Implementation done but needs validation
Code of Conduct

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