mcp_howtocook_whatToEat

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool recommends dish combinations based on people count, but doesn't describe how recommendations are generated (e.g., random, curated, based on preferences), what the output format is, or any constraints like rate limits or authentication needs. For a tool with no annotations, this is a significant gap in transparency.

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 concise and front-loaded: a single sentence in Chinese that directly states the tool's function and key parameter. There is no wasted text, and it efficiently communicates the core idea without unnecessary elaboration.

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 (recommendation based on a single parameter), no annotations, and no output schema, the description is minimally adequate. It covers the purpose and parameter intent but lacks details on behavior, output, or differentiation from siblings. This meets the minimum viable threshold but has clear gaps in completeness.

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 description adds meaningful context beyond the input schema. The schema describes 'peopleCount' as an integer 1-10 for meal count, but the description clarifies it's for '用餐人数' (dining people) and that it '会根据人数推荐合适数量的菜品' (will recommend suitable number of dishes based on people count), explaining the parameter's purpose in the recommendation logic. With 100% schema coverage and 1 parameter, this exceeds the baseline of 3.

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: '根据人数直接推荐适合的菜品组合' (recommend suitable dish combinations based on number of people). It specifies the verb '推荐' (recommend) and the resource '菜品组合' (dish combinations), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'mcp_howtocook_recommendMeals', which appears similar.

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 minimal guidance: it implies usage when '不知道吃什么' (don't know what to eat) and based on '人数' (number of people). However, it offers no explicit when-to-use vs. when-not-to-use instructions, no prerequisites, and no alternatives compared to sibling tools like 'mcp_howtocook_recommendMeals'. This leaves the agent with unclear decision criteria.

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