Critical gaps across all dimensions. Tool definitions are proxied from a remote server and not directly visible in the source code, only tool names and descriptions are inferred from comments. No input schemas, parameter descriptions, or output schemas are defined in the MCP server itself. The server acts as a thin JSON-RPC passthrough (mcp_stdio.py) without schema validation, documentation, or error guidance. Descriptions are minimal (10-50 chars) and lack context on when to use each tool or what they return. No parameter documentation exists. Error handling is generic (repr(e)) with no recovery guidance. This violates 40+ patterns from the Arcade rubric.

• Define explicit input schemas for all tools in mcp_stdio.py. For forum_search, include: query (string, required, 1-500 chars), limit (integer, 1-100, default 20), offset (integer, default 0). Return schema with fields: posts (array of {id, title, author, created_at, snippet}).
• Expand tool descriptions to 100-200 chars following WHAT/WHEN/RETURN pattern. Example: 'Search forum posts by keyword. Use this to find existing discussions before posting. Returns up to 20 matching posts with title, author, and preview. Requires exact or partial keyword match.'
• Document all parameters in tool schemas with type, description, and constraints. Example: 'query (string, 1-500 chars): Search term or phrase. Matches against post titles and content.'
• Implement structured error responses with error codes, user-fixable guidance, and retry hints. Example: '{"error": "auth_required", "message": "FORUM_TOKEN not set. Set FORUM_TOKEN env var to enable forum_post and forum_reply.", "retryable": false}'
• Add output schema documentation in tool descriptions or a separate schema file. Specify what fields each tool returns (e.g., forum_read returns {id, title, author, content, created_at, reply_count}) so LLM knows what data is available for downstream use.