EKMS MCP Server

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does describe key behavioral aspects: the tool personalizes recommendations based on personality traits and health scores, and explains how different trait levels affect recommendations (e.g., high openness = extended information, low = core answers). However, it doesn't mention what format the recommendations come in, whether there are rate limits, authentication requirements, or potential side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded with the core purpose in the first sentence. The bullet points efficiently explain the personality traits without unnecessary elaboration. There's minimal waste, though the health score explanation could be slightly more concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (5 parameters including nested objects) and absence of both annotations and output schema, the description does a reasonable job but has gaps. It explains the personalization logic well but doesn't describe the return format, error conditions, or what constitutes 'knowledge' in this context. For a recommendation tool with no output schema, more information about expected outputs would be helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema by explaining the semantic meaning of personality traits: how openness affects information depth preference, conscientiousness affects detail level preference, and neuroticism affects reassurance needs. This provides valuable guidance for parameter interpretation that isn't in the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: '根據對話上下文和客戶特徵推薦相關知識' (recommend relevant knowledge based on conversation context and customer characteristics). It specifies the verb '推薦' (recommend) and resource '知識' (knowledge), and distinguishes itself from siblings like get_knowledge_item (retrieve specific item) or search_knowledge (general search) by focusing on personalized recommendations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool: when you need '個人化推薦' (personalized recommendations) based on Big Five personality traits and health scores. It doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools, but the context is sufficiently clear for an agent to understand this is for recommendation rather than retrieval or listing operations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.