Strong schema definitions with comprehensive parameter documentation across all 10 tools. All tools have clear, actionable descriptions (100-250 chars). Input schemas are well-structured using Zod with proper type definitions and descriptions. However, output schemas are not formally documented, responses are formatted as markdown text rather than structured JSON. Tool names are descriptive but lack action verbs (analyze_image vs vision_analyze). Error handling is present but generic. No tool annotations (readOnlyHint, destructiveHint) despite all tools being read-only.

• Document output schemas formally. Define a response object with typed fields (e.g., {analysis: string, confidence: number, labels: string[]}). Return structured JSON instead of markdown-formatted text to enable downstream tool chaining.
• Add readOnlyHint: true to all tool definitions in server registration. This signals to agents that these tools have no side effects and are safe to call speculatively.
• Enhance error messages with recovery guidance. Instead of 'Vision analyze_image failed: timeout', return 'Image analysis timed out (retryable). Try reducing image size with detail="low" or check network connectivity.'
• Rename tools to follow verb_noun convention: vision_analyze, vision_describe, vision_ocr, vision_compare, vision_refer, vision_review_ui, vision_read_chart, vision_explain_diagram, vision_extract, vision_status. This improves LLM intent parsing.
• For extract_from_image, accept schema as a structured object parameter (type: object) rather than a string. Validate schema structure server-side and return clear validation errors if the schema is malformed.