Server has 7 tools with basic schemas and descriptions, but significant quality gaps limit production readiness. All tools have descriptions (10-100 chars, mostly adequate) and input schemas with typed parameters. However, parameter descriptions are sparse or missing context; output schemas are undocumented; error handling is minimal; and tool naming lacks action verbs. No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite all being read-only operations. Average tool score: 52/100.

• Rename tools to use consistent action verbs: 'create_tech_article_schema', 'create_faq_page_schema', 'validate_ga4_metrics', 'compute_content_stickiness_score', 'audit_llm_txt', 'audit_security_txt', 'extract_answer_snippets'. This improves LLM intent inference.
• Document output schemas in tool docstrings. Example: 'Returns: {status: str, schema_type: str, json_ld: dict}' for generate_tech_article_schema. This enables agents to plan downstream calls.
• Add format constraints to parameter descriptions. Example: 'questions_answers_json: JSON string containing array of objects with required keys "question" (string) and "answer" (string). Example: '[{"question": "What is X?", "answer": "Y"}]'.
• Annotate all tools with @mcp.tool(readOnlyHint=True) to signal they are safe to retry and do not modify state. This guides agent retry logic.
• Add input validation and actionable error messages. Example: validate_ga4_report_metrics should reject empty dimension/metric lists and return 'At least one dimension and one metric required. Received 0 dimensions, 0 metrics.' instead of silently validating empty input.