Single tool 'summarize_tag_data' has a reasonable name (verb_noun) and adequate description (95 chars), but critical gaps undermine quality. Input schema is present with three parameters (tagname, plantcode, days_delta), all with type definitions and descriptions. However, the schema lacks critical constraints: no enums for environment selection (hardcoded 'STAGING'), no min/max bounds on days_delta (unbounded integer invites absurd values), no format specification for comma-separated inputs. Output is returned as raw JSON string with no documented schema, LLM cannot predict structure. No error handling guidance, no recovery paths, no validation of input format. The tool accepts comma-separated values but provides no validation or error message if parsing fails. Security concern: env.json loaded from filesystem with hardcoded path, credentials embedded in code rather than injected.

• Document output schema explicitly: 'Returns dict[str, dict[str, float]] where keys are tag names and values are {timestamp: value} mappings. Example: {"TAG_A": {"2024-01-15T10:30:00": 42.5}}'
• Add min/max constraints to days_delta: change description to 'Number of days to look back from current time (1-365 days). Determines granularity: ≤14 days = minute, 15-30 days = hour, >30 days = day.'
• Add format specification for comma-separated inputs: 'Comma-separated list of tag names (e.g., TAG_A,TAG_B,TAG_C). No spaces around commas.'
• Implement input validation with actionable errors: catch parsing failures and return 'Invalid plant code format. Expected comma-separated list like PLANT1,PLANT2. Got: [input]'
• Move credentials to environment variables: replace hardcoded env.json with os.getenv('CUBE_SECRET'), os.getenv('CUBE_API_URL'). Expose environment selection as optional parameter with enum: environment in ['STAGING', 'PRODUCTION']