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. However, it only states the action ('get all recipes') without any details on traits like whether it's read-only, destructive, requires authentication, has rate limits, returns paginated results, or what the output format might be. For a tool with zero annotation coverage, 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 extremely concise—a single phrase—and front-loaded with the core action. There is no wasted language or unnecessary elaboration, making it efficient in terms of brevity, though this comes at the cost of completeness.

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 complexity (a tool to retrieve all recipes), lack of annotations, low schema coverage (0%), and no output schema, the description is incomplete. It fails to address key contextual aspects like behavioral traits, parameter usage, output expectations, or differentiation from siblings, making it inadequate for effective tool invocation.

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 input schema has one parameter ('no_param') with 0% description coverage, and the tool description provides no information about parameters. The description doesn't explain what 'no_param' is, why it exists, or how it affects the operation. With low schema coverage and no compensation in the description, parameters remain undocumented.

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 '获取所有菜谱' (Get all recipes) restates the tool name 'getAllRecipes' almost verbatim, making it tautological. While it indicates the resource (recipes), it lacks specificity about what 'all' entails (e.g., pagination, filtering) and doesn't distinguish it from sibling tools like 'getRecipesByCategory' or 'recommendMeals'. This is a minimal restatement rather than a clear purpose definition.

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 sibling tools like 'getRecipesByCategory' for filtered results or 'recommendMeals' for suggestions, nor does it specify any prerequisites, exclusions, or contexts for usage. This leaves the agent without direction on tool selection.

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

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 describes a read operation ('查询' - query) and specifies the return content ('完整详情，包括食材、步骤等' - complete details including ingredients, steps, etc.), which is helpful. However, it lacks details on error handling (e.g., what happens if the recipe isn't found), response format, pagination, or rate limits. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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 front-loads the core purpose: '根据菜谱名称或ID查询指定菜谱的完整详情，包括食材、步骤等'. It avoids unnecessary words and directly states what the tool does. However, it could be slightly more structured by explicitly separating the query method from the returned details, but overall, it's concise and to the point.

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 context: no annotations, no output schema, 1 parameter with 0% schema coverage, and sibling tools, the description is incomplete. It adequately explains the basic purpose but lacks details on behavioral aspects (e.g., error handling), parameter semantics (e.g., query format), and differentiation from siblings. For a tool that retrieves specific data, more context is needed to ensure the agent can use it correctly without guesswork.

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 input schema has 1 parameter ('query') with 0% description coverage, meaning the schema provides no semantic information. The description adds some value by explaining that 'query' accepts '菜谱名称或ID' (recipe name or ID), clarifying its purpose. However, it doesn't specify format constraints (e.g., whether ID is numeric or string, if name matching is case-sensitive or supports partial matches), which is a notable gap given the low schema coverage. This partially compensates but falls short of fully documenting the parameter.

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: '根据菜谱名称或ID查询指定菜谱的完整详情，包括食材、步骤等' (Query the complete details of a specified recipe by recipe name or ID, including ingredients, steps, etc.). It specifies the verb ('查询' - query), resource ('菜谱' - recipe), and scope ('完整详情' - complete details). However, it doesn't explicitly differentiate from siblings like 'mcp_howtocook_getAllRecipes' (which likely lists all recipes) or 'mcp_howtocook_getRecipesByCategory' (which likely filters by category), though the focus on a single recipe by name/ID is implied.

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 on when to use this tool. It mentions querying by '菜谱名称或ID' (recipe name or ID), which implies it's for retrieving details of a specific recipe. However, it doesn't explicitly state when to use it versus alternatives like 'mcp_howtocook_getAllRecipes' (for listing all recipes) or 'mcp_howtocook_getRecipesByCategory' (for recipes in a category), nor does it mention prerequisites or exclusions. Usage is implied but not clearly articulated.

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

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 only states what the tool does (query by category) and lists categories, but doesn't describe return format, pagination, error handling, rate limits, or authentication needs. For a query tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.

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 concise—a single sentence stating the purpose followed by a list of categories. Every element serves a purpose: the first part defines the tool's function, and the second part provides essential parameter context. No wasted words, though it could be slightly more structured.

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 complexity (a query tool with one parameter), no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., recipe list format), error conditions, or how it differs from siblings. While it covers basic purpose and parameter values, it lacks sufficient context for reliable agent use.

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 0%, so the description must compensate. It adds value by listing the available categories ('水产, 早餐, 调料, 甜品, 饮品, 荤菜, 半成品加工, 汤, 主食, 素菜'), which clarifies the expected values for the 'category' parameter. However, it doesn't explain parameter format, constraints, or provide examples, leaving some ambiguity.

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: '根据分类查询菜谱' (query recipes by category). It specifies the verb '查询' (query) and resource '菜谱' (recipes), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'mcp_howtocook_getAllRecipes' (which presumably gets all recipes without filtering), so it doesn't reach the highest score.

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 lists available categories but doesn't mention sibling tools like 'mcp_howtocook_getAllRecipes' (for unfiltered queries) or 'mcp_howtocook_getRecipeById' (for specific recipes). There's no context about prerequisites, limitations, or typical use cases.

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

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

With no annotations provided, the description carries full burden but lacks behavioral details. It mentions '智能推荐' (intelligent recommendation) but doesn't disclose how recommendations are generated, whether they're personalized, what timeframes are considered, or what format the output takes. For a planning tool with zero annotation coverage, 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 Chinese sentence that front-loads the core functionality. Every element ('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') earns its place by specifying inputs and multiple outputs without redundancy.

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 3-parameter planning tool with no annotations and no output schema, the description is incomplete. It outlines the high-level purpose but lacks crucial details about how the tool behaves, what the outputs look like, how recommendations are tailored, or any limitations. The agent would struggle to use this effectively without trial-and-error.

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 0%, so the description must compensate but adds minimal parameter semantics. It mentions '忌口' (avoidItems), '过敏原' (allergies), and '人数' (peopleCount) which map to the three parameters, but provides no details about expected formats (e.g., string arrays for allergies), constraints, or how they influence recommendations. The description doesn't adequately compensate for the complete lack of schema documentation.

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 with specific verbs ('智能推荐菜谱', '创建一周的膳食计划', '创建大致的购物清单') and resources ('菜谱', '膳食计划', '购物清单'). It distinguishes from sibling tools like 'getAllRecipes' or 'getRecipeById' by emphasizing meal planning and shopping list generation rather than simple recipe retrieval.

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 implies usage context through the mention of '用户的忌口、过敏原、人数', suggesting it should be used when dietary restrictions and group size are known. However, it doesn't explicitly state when NOT to use this tool or name alternatives like 'mcp_howtocook_whatToEat' for simpler recommendations.

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

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

No annotations are provided, so the description carries full burden. It mentions '推荐' (recommend) but doesn't disclose behavioral traits such as whether this is a read-only operation, how recommendations are generated, potential limitations, or what the output format looks like. For a tool with zero annotation coverage, this is a significant gap.

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: a single sentence that directly states the tool's purpose without unnecessary words. Every part earns its place, making it efficient and clear.

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 complexity (a recommendation tool with 1 parameter), no annotations, no output schema, and 0% schema coverage, the description is incomplete. It lacks details on how recommendations work, output format, or differentiation from siblings, making it inadequate for an agent to use effectively without additional context.

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 some meaning beyond the input schema: it explains that 'peopleCount' is used to '根据人数' (based on number of people) for recommendations. However, with 0% schema description coverage and only 1 parameter, the baseline is 4, but the description doesn't fully compensate by detailing constraints like valid ranges or examples, so a 3 is appropriate.

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 resource '菜品组合' (dish combinations), though it doesn't explicitly distinguish 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 number of people. However, it offers no explicit when-to-use vs. alternatives, no prerequisites, and doesn't differentiate from sibling tools like mcp_howtocook_recommendMeals, leaving the agent to guess.

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