The server provides 4 well-defined tools with clear action verbs and structured input schemas. All tools have descriptions, but descriptions are formulaic and lack LLM-optimization guidance (no 'when to use' or 'what to do next' hints). All parameters have types and descriptions, which is strong. However, output schemas are not documented, the tools return JSON strings without specifying the structure of that JSON. Error handling uses inline validation but does not return actionable recovery guidance. The tooling is competent for a financial data aggregator but misses several production-grade patterns.

• Document the output schema for each tool. Include a JSON Schema or description of the returned object structure. For example, analyze_japanese_company should document that it returns {company_info, edinet_data, tdnet_disclosures, stock_price, error?}.
• Enhance error messages with recovery hints. Instead of 'Invalid stock code: "123" (expected 4 digits)', return 'Invalid stock code: "123" (expected 4 digits). Try check_data_sources() to verify available companies.'
• Add tool annotations to the FastMCP tool registration. Use @mcp.tool() decorators or the fastmcp tool metadata to mark all tools with readOnlyHint=true. This signals to LLMs that these tools are safe to invoke without confirmation.
• Improve tool descriptions with WHEN and WHY context. Example: 'Call this to get a comprehensive financial snapshot of a Japanese company. Use this when you need all available data (financials, disclosures, stock price) in one call. Returns structured analysis ready for agent-to-user reporting.'
• Document the 20-code limit for monitor_earnings in the 'codes' parameter description: 'List of 4-digit stock codes (max 20 for performance reasons).'
• Add a note about pagination/result limits for get_macro_snapshot. If e-Stat results are capped, document: 'Returns up to 50 most-relevant e-Stat datasets matching the keyword. Use more specific keywords to narrow results.'
• Consider splitting analyze_japanese_company into smaller tools if the implementation supports partial queries. E.g., get_company_financials (EDINET), get_company_disclosures (TDNET), get_company_stock_price (separate) would give LLMs finer-grained control and allow partial results on failure.

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry