The server provides 7 well-structured cryptographic utility tools with complete JSON Schema definitions and reasonable descriptions. All tools follow a consistent verb_noun naming pattern (generate_*). However, output schemas are completely undocumented, the tool definitions declare what inputs are accepted but never specify what the response will contain. Parameter descriptions are present but generic; none explain WHEN to use each tool or HOW its output chains to downstream tools. Error handling is not visible in the provided source code. The server lacks documentation of which tools are idempotent, read-only, or destructive (though all appear READ_ONLY). No evidence of pagination, rate limiting, or result capping for tools that could produce large outputs (e.g., generate_random_string with count=100 produces 100 strings, unbounded token cost).

• Document output schemas for all tools. For example, generate_random_integer should return: {integers: [1, 42, 87], count: 3, generated_at: '2025-01-15T12:34:56Z'}. Include all fields the LLM might need to reason about the result.
• Expand tool descriptions to 100 - 200 characters and include WHEN and WHY. Example: 'Generate cryptographically secure random integers for lottery draws, shuffling, or sampling. Use this when you need unpredictable whole numbers; use generate_random_choice for selecting from a known list. Min/max define the inclusive range.'
• Add parameter-level error handling documentation. Example: 'If min > max, the tool will return error: "Invalid range: min (X) exceeds max (Y)". Swap the values and retry.'
• Document result limits and token costs. Add: 'Generating count > 50 items may exceed context limits. For large batches, call the tool multiple times with smaller count values.'
• Add tool annotations to the MCP spec: mark all tools with readOnlyHint: true and idempotentHint: true. This enables agents to safely retry without worrying about side effects.
• Consider adding a tool like get_randomness_info that returns supported charsets, encoding types, and maximum safe batch sizes. This discovery tool would help agents avoid invalid parameter combinations.
• In parameter descriptions, specify the purpose of numeric constraints. Example: 'count (1 - 1000, default 1), number of values to generate. Limit 1000 to prevent excessive output; use multiple calls for larger batches.'