Server has 10 tools with consistent naming (all verb_noun pattern: get_*) and complete input schemas with type definitions. However, descriptions are generic and lack LLM-optimization guidance. Parameter descriptions are present but minimal (10-30 chars typical). Output schemas are not documented, tools return JSON strings without field-level documentation. Error handling is basic (generic 'Unable to fetch' messages). No tool annotations (readOnlyHint, destructiveHint). Composition is sound (each tool does one thing), but descriptions don't explain WHEN to use each tool vs. alternatives (e.g., get_crypto_prices vs get_historical_crypto_prices are confusingly similar).

• Document output schemas for all tools. Specify the structure of returned JSON (e.g., income_statements is an array of {ticker, period, date, revenue, net_income, ...}). This enables LLMs to extract specific fields and chain tools.
• Consolidate or rename get_crypto_prices and get_historical_crypto_prices. If they are identical, use one name. If different, clarify in descriptions (e.g., 'get_crypto_prices: latest snapshot' vs 'get_historical_crypto_prices: time-series data').
• Expand descriptions to 50-150 chars with context. Example: 'Get income statements for a company. Use this to analyze revenue, expenses, and profitability trends. Requires a valid ticker symbol (e.g., AAPL). Returns annual, quarterly, or TTM data.'
• Implement actionable error messages. Instead of 'Unable to fetch income statements', return 'Invalid ticker: INVALID. Valid tickers are 4-5 uppercase letters (e.g., AAPL, GOOGL). Call get_available_tickers() to discover valid symbols.' or 'API rate limit exceeded. Retry in 60 seconds.'
• Add tool annotations to schema. Mark all tools with readOnlyHint=true to signal they are safe to retry. This helps LLMs optimize planning and error recovery.

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

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