This server has critical gaps in tool definition quality. Of 3 tools examined, all lack comprehensive input schema documentation, parameter descriptions, and output schemas. Tool names are somewhat descriptive but not verb-prefixed conventions. ExplainVocab has a trivial input schema (only 'word' string with minimal description). vocab_agent and math_agent accept a generic 'query' parameter with no constraints, validation guidance, or output structure documented. Descriptions exist but are brief (15-62 chars) and lack LLM-optimization guidance (WHEN to use, WHAT is returned, prerequisites). No error handling guidance visible. No input validation or security considerations documented. No tool annotations (readOnlyHint, etc.). The codebase shows agent-based composition but tool definitions are underspecified for reliable LLM invocation.

• Rename tools to use verb_noun convention: 'explain_vocab' (was ExplainVocab), 'search_vocabulary' or 'lookup_word' (was vocab_agent), 'solve_math' or 'calculate_math' (was math_agent).
• Expand descriptions to 50 - 200 characters and include WHEN (vs alternatives), WHAT (return format), and any prerequisites. Example: 'explain_vocab: Look up the meaning and pronunciation of an English word. Returns definition, part of speech, and example usage.'
• Document output schema for each tool: specify whether it returns a string, object with named fields, or array. Include field names and types in the description or a formal schema block.
• Add parameter constraints: specify 'word' expects 1 - 50 chars, English letters only; 'query' expects clear, complete English math or vocab questions (50 - 500 chars). Add enums if applicable (e.g., math categories: algebra, geometry, calculus, arithmetic).
• Add error handling guidance: 'If word not found, returns null or 'No definition found'. Try a synonym or check spelling.' This tells LLMs how to recover.
• Declare tool annotations: all three tools are read-only (add readOnlyHint: true) and idempotent (idempotentHint: true). This aligns with current MCP spec.
• Document dependencies between tools: vocab_agent calls explain_vocab internally, make this explicit so agents understand the composition.
• Add input validation examples: reject non-ASCII characters, numeric-only queries, or URLs in 'word' parameter with clear error messages.