Critical failures across all dimensions. All three tools have descriptions under 20 chars (hard cap rule applies). Tool names lack action verbs (vapt_reporter, development_reporter, deployment_reporter are noun-based, not verb_noun). Parameters lack type hints in function signatures (feature_name and environment have no type annotations). No input schemas visible in code, parameters are inferred from function args, not explicitly registered. SQL injection vulnerabilities in all tools (string interpolation in queries). No output schema documentation. No error handling beyond basic length checks. No pagination despite tools returning potentially large result sets.

• Rename tools to verb_noun: get_vapt_report, get_development_report, get_deployment_report. This matches the 90% baseline for A+ tools and clarifies intent for LLMs.
• Expand all descriptions to 50-200 chars. Example: 'Retrieve the latest VAPT security report for an asset. Returns vulnerability count, severity distribution, and remediation status. Call this to assess security posture before deployment.'
• Add explicit type hints to all parameters: feature_name: str = '', environment: str = ''. Ensure FastMCP registers these as JSON Schema with descriptions for each param.
• Replace all f-string SQL queries with parameterized queries using SQLAlchemy: engine.execute(text('SELECT * FROM vapt_reports WHERE asset_name = :asset_name'), {'asset_name': asset_name}). This eliminates SQL injection.
• Document output schema for each tool. Example: 'Returns list of report records: [{asset_name: str, vulnerability_count: int, severity: enum(critical|high|medium|low), updated_at: ISO8601, ...}]'. Include pagination (limit, offset) and result count.