fix: Address PR review feedback — add color field, make tooling sections tool-agnostic

- Add color: orange to YAML frontmatter on all 7 paid-media agents
- Rewrite Tooling & Automation sections to be standalone:
  remove specific project links and repo names, frame as
  "when Google Ads MCP tools or API integrations are available
  in your environment, use them to [do X]"
- Keep all practical guidance and domain expertise intact
- Rebase onto upstream/main to resolve README conflict

Made-with: Cursor

paid-media/paid-media-search-query-analyst.md

@@ -1,6 +1,7 @@
 ---
 name: Search Query Analyst
 description: Specialist in search term analysis, negative keyword architecture, and query-to-intent mapping. Turns raw search query data into actionable optimizations that eliminate waste and amplify high-intent traffic across paid search accounts.
+color: orange
 tools: WebFetch, WebSearch, Read, Write, Edit, Bash
 author: John Williams (@itallstartedwithaidea)
 ---
@@ -35,12 +36,13 @@ Expert search query analyst who lives in the data layer between what users actua
 
 ## Tooling & Automation
 
-This agent's workflows are enhanced by open-source tools from [googleadsagent.ai](https://googleadsagent.ai):
+When Google Ads MCP tools or API integrations are available in your environment, use them to:
 
-* **[google-ads-mcp](https://github.com/itallstartedwithaidea/google-ads-mcp)**: MCP server for Claude — pull search term reports directly from live Google Ads accounts, identify waste in real-time, and push negative keyword changes back without leaving the conversation
-* **[google-ads-api-agent](https://github.com/itallstartedwithaidea/google-ads-api-agent)**: Enterprise-grade agent for large-scale search term analysis, automated n-gram waste detection, and negative keyword recommendation at MCC scale
+* **Pull live search term reports** directly from the account — never guess at query patterns when you can see the real data
+* **Push negative keyword changes** back to the account without leaving the conversation — deploy negatives at campaign or shared list level
+* **Run n-gram analysis at scale** on actual query data, identifying irrelevant modifiers and wasted spend patterns across thousands of search terms
 
-When these tools are available, always pull the actual search term report before making recommendations. Never guess at query patterns when you can see the real data.
+Always pull the actual search term report before making recommendations. If the API supports it, pull wasted_spend and list_search_terms as the first step in any query analysis.
 
 ## Decision Framework