Server has 13 tools with complete JSON schemas and type definitions. All tools have descriptions (10-50 chars, mostly short). Tool annotations (readOnlyHint) are applied to 12/13 tools. However, descriptions are uniformly terse (e.g., '統計表情報を検索する.' = 'Search for statistical table information.'), providing minimal context for LLM selection. Parameter descriptions are present but generic ('分類事項01のコード（絞り込み用）' = 'Classification item 01 code (for filtering)') without explaining when/why to use them. No output schemas documented. Error handling is absent from visible code. Naming is verb-first and clear (get_*, search_*, post_*), following conventions well. Composition is reasonable, tools are single-purpose and chainable (search → get_meta_info → get_stats_data). No secrets exposed as parameters (API key via E_STAT_APP_ID env var). Overall: solid structural foundation, but descriptions lack LLM-optimization depth and output schemas are undocumented.

• Expand tool descriptions to 80-150 chars. Include WHEN to use (e.g., 'Use this to find available statistical tables by keyword before fetching data'), WHAT it returns (e.g., 'Returns list of table IDs, names, and survey years'), and any prerequisites (e.g., 'Requires valid E_STAT_APP_ID environment variable').
• Add format constraints to parameter descriptions. E.g., 'cdcat01: Classification code (2-4 digits, e.g., 01, 0101). Leave empty to include all categories.' This prevents LLM hallucination of invalid codes.
• Document output schemas for each tool. Add a 'Returns' section to docstrings: 'Returns: {"result": [{"id": string, "name": string, "year": integer}], "count": integer}'. This enables LLMs to chain tools correctly.
• Consolidate JSON/CSV variants into single tools with a 'format' enum parameter (default: json). Reduces tool count from 13 to 8, simplifies LLM reasoning, and follows single-responsibility principle.
• Add error handling examples to tool docstrings. E.g., 'If stats_data_id is invalid, returns error. Try get_stats_list() first to find valid IDs.' This guides LLM recovery paths.