This server has clear, well-structured tool definitions with reasonable naming and descriptions. All 5 tools are explicitly registered in TOOLS array with names, descriptions, and inputSchemas. However, parameter descriptions are missing, the rubric requires every parameter to have a description explaining what it controls. Input schemas lack property descriptions for all parameters (query, limit, season, year, term, name). This is a CRITICAL gap: without parameter descriptions, LLMs cannot infer valid values or constraints. All tools are READ_ONLY with no state modification, which is appropriate. The descriptions are reasonably detailed (100-150 chars avg) and explain WHAT the tool does and WHEN to use it. Error handling is basic, responses use jsonOut() wrapper with minimal recovery guidance. No structured error classification or retry hints. Output schemas are not explicitly documented in the code, though the gql() function returns typed responses. Overall composition is clean: each tool has one responsibility, tool names start with action verbs (search_, get_, explain_), and there is clear separation of concerns.

• Add descriptions to all input parameters in inputSchema. Example: { 'query': { 'type': 'string', 'description': 'Anime title or keyword to search (1-100 chars)' }, 'limit': { 'type': 'number', 'description': 'Maximum results (default 5, range 1-10)' } }
• Document output schema for each tool. Add a 'returns' field to each tool definition or include in description. Example: 'Returns object with fields: query (string), count (number), results (array of {title_english, title_japanese, genres, episodes, status, score, season, studio, synopsis, url})'.
• Add recovery guidance to error responses. When a character is not found, return: { 'error': 'Character "X" not found', 'suggestion': 'Try searching with a shorter name or alternative spelling', 'recoverable': true }
• Validate and document parameter ranges. State in descriptions: 'limit: 1-10 (default 5)', 'season: one of WINTER, SPRING, SUMMER, FALL', etc.
• Include chaining IDs in responses. If a downstream tool might need anime_id or manga_id, include them in results so LLM can chain calls without extra lookups.
• Add result count caps to descriptions. E.g., 'Returns up to 10 results sorted by popularity. Large result sets are truncated to conserve tokens.'
• Implement basic result limiting with pagination hints. If over 10 results available, add 'more_available: true' or a 'next_offset' field so agents know more data exists.