Critical failures across all dimensions. The single tool 'execute_skill_action' has a generic, non-verb name that violates naming conventions. Description is minimal (67 chars, below 10-char baseline for clarity). Input schema exists but parameters lack descriptions entirely, LLMs cannot infer what query_dense_vector_dim, top_k_neighbors, or ef_search_depth actually control. No output schema documented. The mcp_server.py implementation is a stub that prints JSON and exits; it does not actually implement the MCP protocol or handle tool invocations. Tool definition appears inferred from client.py rather than explicitly registered in the server. No error handling, no validation, no recovery guidance.

• Rename tool to 'search_vector_neighbors' or 'query_hnsw_index', start with a clear action verb that conveys intent.
• Add descriptions to all input parameters: query_dense_vector_dim (e.g., 'Dimensionality of the query vector (typically 768 or 1536)'), top_k_neighbors (e.g., 'Number of nearest neighbors to return (1-1000)'), ef_search_depth (e.g., 'HNSW search depth parameter controlling recall vs latency tradeoff (1-500)').
• Document the output schema with field types and descriptions: search_operation_id (string), index_vectors_count (integer), nearest_neighbors_returned_count (integer), query_latency_micros (integer), recall_rate_estimate_pct (float), vector_index_manifest_url (string).
• Implement the MCP protocol in mcp_server.py: add a request/response loop that parses incoming tool calls, invokes the client, and returns structured responses. Use the official MCP Python SDK or implement JSON-RPC 2.0 message handling.
• Add error handling: validate input ranges (top_k_neighbors 1-1000, ef_search_depth 1-500), return actionable errors if validation fails (e.g., 'Invalid top_k_neighbors: 5000 exceeds maximum of 1000'), and document retry behavior for transient failures.