VectorDB MCP server has basic tool definitions but significant quality gaps. All three tools have descriptions and input schemas, but descriptions are generic (avg 95 chars, below 194-char baseline), parameter descriptions lack detail, and output schemas are undocumented. No error handling guidance, no parameter constraints (enums, ranges), and no security considerations for API key injection. Tool naming is acceptable (verb_noun pattern) but lacks clarity on when to use each tool vs alternatives. The upsert_vectors tool generates IDs internally (str(i)) rather than accepting user-provided identifiers, limiting composability.

• Document output schemas in tool definitions. Add 'Returns: {success: bool, ids: [string], error?: string}' to upsert_vectors and 'Returns: {matches: [{id: string, score: float, metadata?: object}], error?: string}' to search_vectors so LLMs know what fields to expect.
• Enhance parameter descriptions with constraints and examples. E.g., 'top_k: Number of results to return (1-100, default 5). Higher values increase latency.' and 'namespace: Optional string (max 64 chars) to partition vectors. If omitted, uses default namespace.'
• Add error recovery hints. Return structured errors like '{error: "Pinecone connection failed", recovery: "Check PINECONE_API_KEY and PINECONE_ENV environment variables. Retry after 5 seconds."}' instead of raw exception strings.
• Inject Pinecone credentials per-request via tool parameters or secure context, not at module load. This prevents credential leakage if the server is compromised and enables multi-tenant deployments.
• Add input validation with clear error messages. Validate top_k in [1, 100], texts array length ≤ 100, metadata size ≤ 10KB, and return 'Invalid top_k: 1000000. Must be 1-100.' instead of letting invalid calls reach Pinecone.