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

With no annotations, the description carries the full burden. It does disclose one genuine behavioral trait — that it consumes no MCP calls (i.e. no cost/rate-limit concern) — which is valuable. But it says nothing about output format, permissions, or whether it is read-only, leaving a modest 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?

A single, front-loaded sentence with the verb and topic stated first and the cost note second. No wasted words, though it is terse enough that it stops short of explaining the return.

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 simple 1-param, 0-required tool with no output schema, the description covers purpose and cost but omits any indication of what the explanation looks like or how detailed it is. Adequate but leaves a gap the missing output schema does not fill.

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 coverage is 0% and there is one enum parameter (topic: neat|met). The description names both 'NEAT' and 'MET', implicitly mapping to the enum values, so it partially compensates. It adds no further meaning about acceptable values or defaults.

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?

States a specific verb (科普/explain) and resource (NEAT / MET 的含义与依据), so the agent knows this is an informational/reference tool rather than a computation. It is clearly distinguishable from siblings like walk_allowance and what_can_i_eat, though it never names them explicitly.

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 clause '不消耗任何 MCP 调用' implies you can call this freely when you need background on NEAT/MET, which is a usable context hint. However, there is no explicit when-to-use/when-not statement and no reference to alternative tools for related questions.

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, the description carries the full burden and does meaningful work: it discloses the cross-source nature, the defaults used when no params are supplied (1500m, batch 1), the pagination protocol via page, and the presence of a 'next' suggestion field in the response. Gaps remain: it never explains how latitude/longitude are obtained (the tool cannot locate the user without them) and says nothing about latency, rate limits, or permissions.

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?

Front-loaded with purpose then trigger conditions, and each bullet maps a distinct user utterance to a distinct parameter, so the length is mostly earned. The '★' mapping block duplicates the per-parameter descriptions in the schema, which is mild redundancy but not padding.

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 6-param, zero-annotation, no-output-schema tool, this covers defaults, pagination, and the source mix well enough to invoke correctly without authoring params. It is incomplete on two counts that matter for a location tool: how coordinates are sourced, and how to choose between this and 'what_can_i_eat'.

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 parameter-level mapping guidance in the description (industry, radius_m=500, page+1) largely restates what the schema already documents per field. It adds one non-schema rule ('fill the param instead of filtering results yourself'), but that belongs more to usage than to parameter semantics, so this sits at the baseline for fully-documented schemas.

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 states a specific verb+resource: it returns nearby stores plus per-dish affordability, and explicitly notes it spans two data sources (McDonald's MCP + local restaurants). That is far more specific than the name alone. However, it never distinguishes itself from the sibling 'what_can_i_eat', which by name sounds like a competing answer to the same user intent, so the agent gets no disambiguation.

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?

Strong trigger coverage: it names concrete user phrasings ('附近有什么能吃的', '现在吃什么', '有什么推荐的') and maps constrained phrasings to specific parameters, plus a 'don't pre-filter results yourself' rule and the no-argument fallback behavior. It stops short of the top mark because it never states when NOT to use it or names an alternative sibling for the overlapping case.

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 behavioral burden, yet it discloses nothing about the underlying model: it never mentions whether user weight/step length are assumed, whether the computation is side-effect-free, or what the result contains. For a calculator tool this is a meaningful gap, since the agent cannot tell what inputs the result actually depends on.

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?

Two tight sentences: the first states the computation and its output, the second gives the invocation condition. Nothing is wasted and the purpose is front-loaded.

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 one-parameter computed tool with no output schema, the description adequately covers what it produces ('available allowance for this meal'). The remaining gap is that the derivation assumptions (calorie-per-step or user profile) are never hinted at, but the tool's surface is small enough that this is minor.

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% (distance_m with '步行距离（米）'), so the schema already documents the single parameter. The description only restates 'given walking distance' without adding units, constraints, or sign/range guidance, so baseline 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 states a specific transformation: given walking distance, compute the calorie allowance available for the next meal. It is clearly distinguishable from siblings like nearby_food or explain_concept, though it never explicitly contrasts itself with what_can_i_eat, which sounds closely related.

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?

It names concrete trigger phrases ('how much can I eat after walking', 'how much allowance is left'), giving the agent a usable activation condition. However, it offers no exclusions or explicit routing away from the sibling tool what_can_i_eat, so the boundary remains inferential.

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. It does disclose the cross-datasource aggregation behavior and, indirectly, that coordinate-free calls degrade to a local snapshot — but it says nothing about return shape, ranking, staleness, or failure modes when quota is omitted.

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?

Front-loaded purpose sentence followed by two compact routing sentences; every clause maps to a decision the agent must make. No filler or restatement of the tool name.

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?

All five parameters are optional and there is no output schema, so return values need not be explained. However, the description never states what happens when quota is omitted or how results are ordered, which matters for a cross-source aggregation tool with zero required inputs.

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 baseline is 3, but the description adds conditional semantics the schema lacks: fill industry only when the user constrains the business type, and the pagination follow-up belongs to nearby_food, not this tool's own parameters.

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?

States a specific verb+resource with scope: what can be eaten within the calorie quota, aggregated across data sources. The parenthetical '(跨数据源)' distinguishes it from a single-source lookup like nearby_food, though the distinction is brief rather than fully drawn.

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?

Explicit trigger phrases ('我现在能点什么', '有什么便宜（低卡）的'), an explicit conditional for filling industry when the user names a business type, and an explicit routing rule to use nearby_food's page parameter on follow-up ('还有吗'). This is close to ideal when/when-not guidance.

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