This server has critical definition failures. The single tool 'execute_skill_action' lacks a visible input schema in the source code, only a description string is present. No parameter definitions, types, or constraints are documented. The mcp_server.py file contains only a stub that prints a JSON status message and does not implement actual MCP protocol handling. The client.py and example_usage.py files show a mock implementation with hardcoded responses, not a real tool interface. Tool descriptions are minimal (under 50 chars). No error handling, output schema documentation, or parameter validation is evident. The server does not implement the MCP protocol correctly, it merely prints a status message to stdout and exits, making it non-functional as an MCP server.

• Define a complete JSON Schema for execute_skill_action input parameters. Include query_embedding, top_k, collection_size, and any other parameters with explicit types (string, number, integer), descriptions, and constraints (min/max, enums).
• Expand tool description to 150-250 characters. Explain: 'Search a vector collection using HNSW nearest neighbor index. Accepts a query embedding and returns the top-k most similar vectors with similarity scores and metadata. Use this to find semantically similar items in a pre-indexed collection.'
• Document the output schema with all returned fields: index_query_id (string), dimension (integer), top_k (integer), collection_vectors_indexed (integer), hnsw_graph_m_parameter (integer), hnsw_ef_construction (integer), recall_rate_at_k_pct (number), query_latency_microseconds (integer), and any result vectors/scores.
• Implement actual MCP protocol in mcp_server.py. Use a proper MCP library (e.g., mcp-python) or implement the JSON-RPC 2.0 message loop to handle initialize, list_tools, call_tool, and other MCP requests over STDIO.
• Rename tool to 'search_vector_collection' or 'query_nearest_neighbors' to clearly signal the action. Add parameter descriptions: 'query_embedding (array of floats): The embedding vector to search for (dimension must match index)', 'top_k (integer, 1-1000): Number of nearest neighbors to return', 'collection_size (integer): Size of the indexed collection for context'.