Three tools with basic schemas and descriptions, but significant gaps in parameter documentation and output schema clarity. Tool names lack action verbs (subjects, table_meta, query_table are noun-heavy). Descriptions are present but terse (10-80 chars), below the 50-200 char baseline for LLM optimization. Parameter descriptions exist but lack detail on constraints, formats, and valid ranges. No output schemas documented. Error handling is minimal, raw HTTP errors with truncated text. The 'body' parameter in query_table is documented only as a JSON example string, not a structured schema. No tool annotations (readOnlyHint, etc.) despite all being read-only operations.

• Rename tools to verb_noun: 'list_subjects', 'get_table_metadata', 'fetch_table_data'. This signals intent and matches LLM expectations.
• Expand descriptions to 100 - 150 chars each. Explain the workflow: 'List available databases and drill into the H2 subject tree to find tables. Call this first to discover table paths for table_meta and fetch_table_data.'
• Document output schemas explicitly. E.g., subjects returns {dbid, items: [{id, type: 'l'|'t', label}]}; table_meta returns {dimensions: [{code, values: [...]}]}; fetch_table_data returns {data: [...], metadata: {...}}.
• Replace the 'body' parameter description with a structured schema. Define query as {query: [{code: string, selection: {filter: 'all'|'item', values: string[]}}], response: {format: 'json-stat2'}} with required fields and type constraints.
• Add error recovery guidance. Catch HTTP 400 (cell limit) and return 'Cell limit exceeded. Reduce selection.values per dimension or request fewer dimensions.' Catch 404 and suggest 'Path not found. Call list_subjects to verify the table path.'