This is a multi-framework SDK collection (CrewAI, LangChain, n8n, Vercel AI) wrapping a Polaris/VEROQ intelligence API. Tools are defined across Python and TypeScript packages but lack MCP server implementation. All 16 tools have descriptions (194-char baseline met) and basic input schemas with type info. However, schemas are minimal, most parameters lack detailed constraints, enums, or validation guidance. No output schemas are documented. Error handling is absent from tool definitions. Tool naming follows verb_noun convention (search, get, extract, verify) but descriptions are generic API docs rather than LLM-optimized. Parameters like 'depth' (fast|standard|deep) and 'verdict' (supported|contradicted|...) should be enums but are free-form strings. No pagination guidance despite tools returning lists. Security (API key injection) is not visible in tool definitions.

• Document output schemas for all tools. Specify fields returned (e.g. brief_id, title, confidence_score, sources[], entities[]) so agents can chain calls and extract data.
• Convert free-form string parameters to enums. Define depth: [fast, standard, deep], verdict: [supported, contradicted, partially_supported, unverifiable], and category as constrained lists in schema.
• Consolidate duplicate tool pairs. Choose one canonical set (veroq_* or polaris_*) or document API differences, coverage, and when to use each. Reduce cognitive load on LLM tool selection.
• Add pagination support. Include limit (default 20, max 100), offset/cursor, and total_count in responses. Document result limits in descriptions to prevent context window exhaustion.
• Add error handling guidance. Return structured errors with error_code, message, and recovery_hint (e.g. 'Invalid category. Try: ai_ml, markets, crypto, policy'). Enable LLM self-correction.