Both tools have critical definition gaps. call_Analytics_Data_engine has a minimal description (47 chars) and an input schema with only a 'scope_object' parameter that lacks a type definition or description. call_Data_Upload_Engine has an empty input schema ({}) and a minimal description (48 chars). Neither tool documents output schemas, error handling, or recovery guidance. Tool names lack clear action verbs (call_* is generic). No parameter descriptions exist. Schemas are incomplete and do not follow JSON Schema conventions with proper type declarations.

• Rename tools to verb_noun format: 'fetch_analytics_data' and 'upload_data'. This clarifies intent and helps LLMs select the right tool.
• Add complete input schemas with proper JSON Schema types. For call_Analytics_Data_engine, define scope_object as an object with typed properties (e.g., {"type": "object", "properties": {"query": {"type": "string", "description": "..."}}}).
• Expand descriptions to 50-200 characters. Current descriptions (47-48 chars) are too terse. Example: 'Fetch analytics data for a given package. Accepts a scope object with query parameters. Returns metrics including views, downloads, and engagement data.'
• Document output schemas explicitly. Specify what fields each tool returns (e.g., {"type": "object", "properties": {"data": {...}, "status": {...}}}). This enables LLMs to chain tools.
• Add error handling guidance. Document expected errors (e.g., 'Invalid scope_object', 'Package not found') and recovery steps (e.g., 'If package not found, try search_packages() first').