Server defines 6 tools with descriptions and basic schemas. Naming follows verb_noun convention (search_web, classify_topic, generate_variants, evaluate_and_select, request_human_approval, publish_to_gamma). Descriptions are present and moderately detailed (100-250 chars typical), explaining WHAT and WHEN. However, parameter descriptions are minimal or absent for several tools (e.g., generate_variants lacks description for 'classification_json' parameter). Output schemas are not documented, callers cannot predict response structure. Error handling is minimal; tools return generic error strings without recovery guidance. No input validation constraints (enums, ranges) visible. Tool composition is reasonable (each does one thing), but chaining relies on implicit JSON field naming conventions rather than explicit schema contracts.

• Document output schemas for all tools. For classify_topic, publish: {topic_type: string, styles: [{name, angle, tone}]}. For generate_variants, publish: {variants: [{id, title, slides: [...]}]}. For evaluate_and_select, publish: {winner_id, winner_content, scores: {...}, reasoning}.
• Add parameter descriptions to all inputs. E.g., 'classification_json: JSON output from classify_topic containing topic_type and styles array. Required to ensure variant generation aligns with routing decision.'
• Add input constraints. E.g., num_slides: integer, min=5, max=50. theme: enum of valid Gamma theme IDs (pearl, dark, minimal, etc.). query: max 200 characters.
• Improve error messages with recovery hints. E.g., 'Search failed: {error}. If this persists, try a simpler query or check your Tavily API key.' For JSON parse errors: 'Classification returned invalid JSON. Ensure the LLM response is valid JSON with required fields: topic_type, styles.'
• Eliminate global state coupling. Pass variants and winning content as explicit parameters or return values. Make each tool call independent and idempotent so agents can retry safely.