generate_enhanced_cv

Instructions

Implementation Reference

• src/cv_resume_builder_mcp/server.py:886-975 (handler)

  The primary handler function for the 'generate_enhanced_cv' tool. Parses existing CV (PDF or LaTeX), fetches recent git commits, achievements from wins.md, Jira tickets, Credly badges, and compiles a detailed report with guidelines for enhancing the CV.

  async def generate_enhanced_cv(existing_cv_path: Optional[str], since: str) -> list[TextContent]:
      """Generate enhanced CV combining all data sources."""
      if not existing_cv_path:
          return [TextContent(type="text", text="Error: existingCvPath parameter is required")]
      
      # Resolve path
      if existing_cv_path.startswith('/'):
          resolved_path = Path(existing_cv_path)
      else:
          resolved_path = Path(REPO_PATH) / existing_cv_path
      
      if not resolved_path.exists():
          return [TextContent(type="text", text=f"CV file not found: {resolved_path}")]
      
      try:
          # Step 1: Parse existing CV
          if resolved_path.suffix.lower() == '.pdf':
              reader = pypdf.PdfReader(str(resolved_path))
              existing_content = ""
              for page in reader.pages:
                  existing_content += page.extract_text() + "\n"
          else:
              existing_content = resolved_path.read_text()
          
          # Step 2: Gather all data sources
          data_sections = []
          
          # Git commits
          git_result = await get_git_log(since)
          data_sections.append(f"\n## Git Commits ({since}):\n{git_result[0].text}")
          
          # Wins
          wins_result = await read_wins()
          data_sections.append(f"\n## Achievements (wins.md):\n{wins_result[0].text}")
          
          # Jira tickets
          if all([JIRA_URL, JIRA_EMAIL, JIRA_API_TOKEN]):
              jira_result = await get_jira_tickets(since, "Done")
              data_sections.append(f"\n{jira_result[0].text}")
          else:
              data_sections.append("\n## Jira Tickets: Not configured")
          
          # Credly badges
          if CREDLY_USER_ID:
              credly_result = await get_credly_badges(50)
              data_sections.append(f"\n{credly_result[0].text}")
          else:
              data_sections.append("\n## Credly Badges: Not configured")
          
          # LinkedIn
          if LINKEDIN_PROFILE_URL:
              data_sections.append(f"\n## LinkedIn Profile: {LINKEDIN_PROFILE_URL}\n(Manual review recommended)")
          
          # Step 3: Compile report
          truncated_cv = existing_content[:2000]
          if len(existing_content) > 2000:
              truncated_cv += "\n... (truncated, full content available)"
          
          report = f"""# Enhanced CV Generation Report
  
  ## Existing CV Content:
  {truncated_cv}
  
  ## Recent Work & Achievements ({since}):
  {''.join(data_sections)}
  
  ---
  
  ## CV Guidelines:
  - Maximum {MAX_BULLETS_PER_EXPERIENCE} bullet points per experience
  - Focus on quantifiable impact and results
  - Use action verbs (Engineered, Designed, Implemented, Led, etc.)
  - Include metrics (percentages, time savings, scale)
  
  ## Next Steps:
  1. Review the existing CV content above
  2. Analyze the recent work data (git commits, Jira tickets, badges)
  3. Identify the top {MAX_BULLETS_PER_EXPERIENCE} most impactful achievements
  4. Generate enhanced CV bullet points in LaTeX format
  5. Ensure each bullet demonstrates clear business value
  
  ## Suggested Approach:
  Ask the AI to:
  "Based on the above data, generate {MAX_BULLETS_PER_EXPERIENCE} enhanced CV bullet points in LaTeX format that highlight my most significant recent achievements with quantifiable results."
  """
          
          return [TextContent(type="text", text=report)]
      
      except Exception as e:
          return [TextContent(type="text", text=f"Enhanced CV generation error: {str(e)}")]

• src/cv_resume_builder_mcp/server.py:282-300 (registration)

  Registration of the 'generate_enhanced_cv' tool in the list_tools() handler, defining its name, description, and input schema.

  Tool(
      name="generate_enhanced_cv",
      description="Generate an enhanced CV by combining existing CV content with data from all sources",
      inputSchema={
          "type": "object",
          "properties": {
              "existingCvPath": {
                  "type": "string",
                  "description": "Path to existing CV (PDF or LaTeX)"
              },
              "since": {
                  "type": "string",
                  "description": "Time range for fetching recent work",
                  "default": "6 months ago"
              }
          },
          "required": ["existingCvPath"]
      }
  )

• src/cv_resume_builder_mcp/server.py:285-299 (schema)

  Input schema definition for the 'generate_enhanced_cv' tool, specifying parameters existingCvPath (required) and since (optional, default '6 months ago').

  inputSchema={
      "type": "object",
      "properties": {
          "existingCvPath": {
              "type": "string",
              "description": "Path to existing CV (PDF or LaTeX)"
          },
          "since": {
              "type": "string",
              "description": "Time range for fetching recent work",
              "default": "6 months ago"
          }
      },
      "required": ["existingCvPath"]
  }

• src/cv_resume_builder_mcp/server.py:362-366 (handler)

  Dispatch logic in the general call_tool handler that routes calls to the specific generate_enhanced_cv function.

  elif name == "generate_enhanced_cv":
      return await generate_enhanced_cv(
          arguments.get("existingCvPath"),
          arguments.get("since", "6 months ago")
      )