This server has 6 simple utility tools with basic but incomplete definitions. All tools have short, clear descriptions (10-40 chars) and properly typed parameters with descriptions. However, the definitions lack production-grade rigor: no output schemas are documented, no error handling guidance exists, and tool purposes are trivial (word counting, math operations). The naming follows verb_noun pattern correctly, but there is no discussion of when these tools should be called vs alternatives, no pagination support, no result limits, and no idempotent/destructive hints. The schema quality is moderate, each parameter has a type and description as required by fastmcp, but responses are undocumented. This is a toy/demo server; definition quality is acceptable for educational purposes but would not pass production code review.

• Document output schemas for each tool. E.g., count_words returns {type: 'integer', description: 'Number of words (non-negative integer)'}. This enables LLMs to plan downstream use.
• Expand tool descriptions to 50-200 chars, stating WHEN to use the tool and what the LLM should expect. E.g., 'Count words in text. Use to measure content length or validate input before processing. Returns non-negative integer; fails if text is None.'
• Add error handling guidance to descriptions. E.g., for count_words: 'If text is empty or None, returns 0. No error raised.'
• Declare tool annotations in fastmcp. Use @mcp.tool(annotations={'readOnly': True}) for read-only tools (all 6 are read-only), so LLMs know they are safe to retry.
• For math tools, document input constraints in parameter descriptions. E.g., 'First number (32-bit signed integer, range -2,147,483,648 to 2,147,483,647)'. Add validation in the tool body to detect out-of-range and return a clear error message.
• Add pagination and result limits if these tools were to scale (not applicable here, but good practice). E.g., if count_words accepted a list of texts, document a limit (e.g., 'Maximum 100 texts per call') and provide offset/limit pagination.
• Consider wrapping math operations into a single 'calculator' tool with an operation parameter (enum: add, subtract, multiply, divide) to reduce tool count and cognitive load for LLMs.
• Add usage examples in expanded descriptions (in text form, not code blocks), so LLMs understand intent. E.g., 'count_words: Use to check if a message is too long. Example: count_words("hello world") → 2.'