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MCP server that generates verified SQL queries from natural language questions using semantic schema understanding and self-learning capabilities
Critical failures across all dimensions. The single tool 'execute_skill_action' has a minimal description (56 chars, below the 10-1024 baseline), lacks output schema documentation entirely, and the input schema is present but sparse. The mcp_server.py implementation is a stub that prints a JSON status message and does not actually register tools, handle MCP protocol messages, or implement the tool execution logic. Tool definitions appear inferred rather than explicitly registered. No error handling, no parameter validation guidance, no security considerations, and no composition patterns evident.
Execute semantic schema self-learning text-to-SQL skill action
Tool description is too short (56 chars) and lacks context. Does not explain WHEN to use this tool, WHAT it returns, or HOW it differs from other SQL generation approaches. LLMs cannot determine tool selection without richer guidance.
Output schema is completely undocumented. The client.py example shows a response with 7 fields (sql_job_id, dialect, generated_sql_query, schema_similarity_score_pct, sql_syntax_compilation_verified, self_learning_context_updated, plotly_chart_json_url), but no formal schema is declared in the tool definition. LLMs cannot plan downstream actions without knowing response structure.
No error handling or recovery guidance. Tool definition does not specify what errors can occur (invalid SQL dialect, malformed question, schema mismatch), how to classify them (retryable vs. fatal), or what the LLM should do next. Agents will be unable to self-correct on failure.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 32 | 2026-07-28+ | v2 |
mcp_server.py is a non-functional stub. It prints a status message but does not implement MCP protocol message handling (initialize, call_tool, etc.). The actual tool execution logic is missing, the server cannot receive or process tool calls from an MCP client.
Parameter descriptions are minimal. 'natural_language_question' and 'database_dialect' have brief descriptions but lack format guidance, constraints, or examples of valid dialects. The 'database_dialect' parameter should declare an enum (POSTGRESQL, SNOWFLAKE, MYSQL, etc.) rather than free-form string.