16 tools with basic descriptions but significant schema and parameter documentation gaps. Tool names follow verb_noun convention (analyze_*, generate_*, suggest_*, etc.), which is good. However, most tools lack visible input parameter schemas with type definitions and descriptions. Descriptions are present but generic (avg ~80 chars, below the 194-char baseline). No output schemas documented. Error handling and recovery guidance absent. Tools appear well-intentioned for SEO workflow but fall short of production-grade definition quality.

• Add explicit input schemas to all 14 tools lacking them. Each schema must include parameter types (string, array, object), descriptions (50-100 chars), and constraints (enums, min/max, patterns). Use Pydantic models or JSON Schema.
• Document output schemas for all 16 tools. Specify return type (object, array, string), field names, types, and descriptions. Include example outputs. This enables agents to chain tools and extract the right data.
• Expand tool descriptions from ~50 chars to 150-250 chars. Include: (1) What the tool does, (2) When to use it vs similar tools, (3) What input is required, (4) What output structure to expect. E.g., 'Analyzes competitor blog posts to identify SEO gaps. Requires competitor URLs. Returns a list of missing topics with search volume and difficulty scores.'
• Add error handling and recovery guidance to each tool. Document common failure modes (network timeout, invalid URL, rate limit) and what the LLM should do (retry, ask user, try alternative). Use pattern:recovery-guide.
• Make tools independent and composable. Remove the rigid workflow assumption. Each tool should work standalone with clear inputs/outputs. Allow agents to call suggest_titles without analyze_site, or audit_content without prepare_draft_skeleton.

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry