Two tools with basic schemas and descriptions, but significant gaps in parameter documentation, output schema clarity, and error handling. Tool names follow verb_noun convention (verify_*, get_*), which is good. However, parameter descriptions are minimal or absent (draft_tokens, draft_probs, target_probs lack descriptions in the schema; uniform_randoms has a description but it's vague). Output schemas are not documented, callers must infer structure from example_usage.py. No error handling guidance. The server implements core logic correctly but fails to meet LLM-friendly documentation standards.

• Add descriptions to all parameters in verify_speculative_tokens inputSchema. Example: 'draft_tokens: List of token strings proposed by the draft model (e.g., ["def", "calculate"]). Length must match draft_probs and target_probs.'
• Document the output schema for both tools. Add a 'returns' section to each tool description or a separate schema field listing field names, types, and meanings. Example for verify_speculative_tokens: 'Returns {accepted_tokens: list[str], num_accepted: int, rejected_at_index: int|null, acceptance_rate: float, effective_tokens_generated: int, gamma: int}'.
• Expand tool descriptions to 150 - 250 characters. Include WHEN to call each tool and what it enables. Example: 'Verify draft tokens using rejection sampling. Call after the draft model proposes K speculative tokens and the target model evaluates them. Returns accepted tokens and rejection index to guide the next decoding step.'
• Add input validation and error messages. Check that draft_probs, target_probs, and uniform_randoms have the same length as draft_tokens. Return a clear error: 'Length mismatch: draft_tokens has 4 items but draft_probs has 3. All arrays must be equal length.'
• Add parameter constraints. Specify that probabilities must be in [0, 1], draft_tokens must be non-empty, and gamma must be a positive integer. Document these in parameter descriptions.