Single tool with clear naming (get_character_count) and adequate description (54 chars). Input schema present with type definitions and parameter descriptions. However, output schema is undocumented (returns int with no structured context), and error handling lacks recovery guidance. No tool annotations. Validation logic exists but error messages are generic. Baseline: avg tool description 194 chars (this is 54); avg params per tool 4 (this has 2). Meets minimum viability but lacks production polish.

• Add structured output schema documentation: 'Returns: {count: int, char: str, text_length: int}' so LLM understands the result context.
• Enhance error messages with recovery guidance: 'Input text exceeds 10,000 chars. Try splitting into smaller chunks or use a substring.' instead of bare ValueError.
• Add tool annotations via FastMCP: @mcp.tool(readOnlyHint=True, idempotentHint=True) to signal safety to agents.
• Expand tool description to 100-150 chars: explain the 10k limit, when to use (counting specific chars in strings), and what the return value represents.
• Add parameter constraints to descriptions: 'char must be exactly 1 character; text must be ≤10,000 chars' to guide LLM input validation.

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

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