mcp_howtocook_recommendMeals

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions creating a weekly meal plan and shopping list, which implies generation/mutation behavior, but doesn't specify whether this creates persistent data, requires authentication, has rate limits, or what the output format looks like. For a tool that appears to generate plans (potentially mutating state), this leaves significant behavioral gaps.

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 a single, efficient sentence that packs substantial information: it covers the input criteria, the core recommendation function, and the two outputs (meal plan and shopping list). There's zero wasted verbiage, and it's appropriately front-loaded with the main purpose.

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?

For a tool with no annotations and no output schema that appears to generate meal plans (potentially a mutation operation), the description is insufficient. It doesn't explain what the output looks like (e.g., format of the meal plan, structure of shopping list), whether the plan is saved or transient, or any error conditions. The completeness is inadequate given the tool's apparent complexity.

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?

Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description mentions the parameters (忌口, 过敏原, 人数) but adds no additional semantic context beyond what's in the schema. The baseline score of 3 is appropriate when the schema does the heavy lifting.

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: '根据用户的忌口、过敏原、人数智能推荐菜谱，创建一周的膳食计划以及大致的购物清单' (Intelligently recommend recipes based on user's dietary restrictions, allergies, and number of people, create a weekly meal plan and approximate shopping list). This specifies the verb (recommend, create) and resources (recipes, meal plan, shopping list). However, it doesn't explicitly distinguish this from sibling tools like 'mcp_howtocook_whatToEat' which might have similar functionality.

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 no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, constraints, or compare it to sibling tools like 'mcp_howtocook_getAllRecipes' or 'mcp_howtocook_whatToEat'. The agent must infer usage from the purpose alone without explicit direction.

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