Critical gaps across all dimensions. Tool definitions are inferred from file paths and brief descriptions only, no explicit schema registration visible in source. Descriptions are extremely terse (10-50 chars), well below the 194-char baseline. No parameter schemas, type definitions, or input validation evident. No output schema documentation. Tools use non-standard naming (native__* prefix, underscores instead of verb_noun). No error handling guidance. This is a custom framework with minimal MCP compliance infrastructure.

• Add explicit JSON Schema input definitions for all tools. Include type, description, enum constraints, and min/max for every parameter. Reference: https://arcade.dev/patterns/constrained-input
• Expand tool descriptions to 100 - 250 characters. Answer: What does it do? When should the LLM call it? What are the side effects? Example: 'Execute PC control actions (mouse clicks, keyboard input, window management). Use when the user wants to interact with the desktop. Returns success/failure and any error details.'
• Rename tools to verb_noun convention: 'execute_pc_action', 'capture_pc_state', 'get_credential', 'send_http_request'. This helps LLMs infer intent from the name alone.
• Document output schemas for each tool. Specify field names, types, and what data is returned. E.g., 'capture_pc_state returns { screenshot: string (base64), accessibility_tree: object, window_list: array }.'
• Add parameter descriptions for all inputs. For 'http_fetch', document: method (GET|POST|PUT|DELETE), url (string, must be HTTPS), headers (object, optional), body (string, optional), timeout_ms (number, 1000 - 30000, default 5000).