Both tools have identical, generic descriptions ('Get a list of internal technical documents for knowledge clustering') that fail to distinguish their purpose or when to use each. Neither tool has an input schema, both declare empty input objects {}. Tool names lack clarity: 'getCompanyKnowledgeDocuments' vs 'searchWiki' do not follow verb_noun convention and are ambiguous about what distinguishes them. No parameter descriptions, no output schema documentation, no error handling guidance. The code shows hardcoded mock data with no actual integration. This server falls well below production baseline.

• Rewrite tool descriptions to be distinct and LLM-optimized (50 - 200 chars each). Example: 'getCompanyKnowledgeDocuments: Retrieve all internal technical documents indexed by department and author. Use this for broad knowledge discovery.' vs 'searchWiki: Search internal wiki documents by title, tags, or author. Use this when you have a specific topic in mind.'
• Add input parameters with types and descriptions. Example: getCompanyKnowledgeDocuments could accept optional 'department' (enum: backend, frontend, game-server, platform, infra, game-design) and 'limit' (1 - 100, default 20). searchWiki could accept 'query' (string, required) and 'tags' (array of strings, optional).
• Document the output schema explicitly. Declare that both tools return an array of objects with fields: title (string), summary (string), tags (array of strings), author (string), createdDate (ISO 8601 date), department (string). Include pagination: total_count (integer), has_more (boolean).
• Implement input validation and error handling. Return structured errors like '{"error": "Invalid department: got \'unknown\'. Must be one of: backend, frontend, game-server, platform, infra, game-design"}' instead of silent failures.
• Rename tools to snake_case and clarify scope. Consider: 'list_knowledge_documents' (returns all) vs 'search_knowledge_documents' (accepts query parameter). Ensure names follow verb_noun convention and are unambiguous.