china-export-rebate-mcp

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

No annotations are provided, so the description carries the behavioral transparency burden. It discloses meaningful behavior: accepted code lengths, prefix expansion to all current valid commodities, and a concrete example. It doesn't mention error handling or output structure, but for a read-only query this is reasonably transparent.

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 two concise sentences plus an example, with no wasted words. The key action and object are stated first, followed by essential format details and a concrete usage illustration.

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 single-parameter tool with no annotations and no output schema, the description covers purpose, input format, prefix behavior, and an example. It could explicitly state return-value structure or error cases, but overall it is sufficiently complete for typical usage.

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 only defines a required string 'hs_code' with zero description coverage. The tool description fully compensates by explaining valid input formats (8/10 digits), prefix behavior (4/6 digits return all items), and providing an example value. This makes the parameter's meaning and constraints clear.

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 opens with a specific verb and resource: '按 HS 编码查询中国出口退税率' (query China export rebate rate by HS code), clearly stating what the tool does. It distinguishes itself from siblings by focusing on HS code lookup rather than statistics or name-based search.

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 gives clear usage context: supports 8/10-digit codes, while 4/6-digit prefixes return all current valid commodities. This conveys when to use the tool, though it doesn't explicitly name alternatives or exclusion cases. The context is strong enough to infer appropriate use.

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 mentions 'current valid' as a filter, but does not disclose whether the operation is read-only, what the aggregation logic is, or any limitations. The description is too minimal to provide meaningful behavioral 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 a single short sentence with no filler. It is concise, front-loaded, and contains no unnecessary information.

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?

Without an output schema, the description should clarify what the distribution overview contains (e.g., buckets, counts, percentages). It only states the general concept, leaving ambiguity about the return value structure and available breakdowns.

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 tool accepts zero parameters, so the schema fully covers the parameter space. The description does not need to explain parameters, and the baseline for 0-parameter tools applies.

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 '当前有效出口退税率分布概览' (overview of current effective export rebate rate distribution), which identifies the resource and output type. It implies differentiation from sibling tools that query specific rates or search by name, though not 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?

No guidance is provided on when to use this tool versus query_rebate_rate or search_rebate_by_name. The description implies an aggregate use case, but there is no explicit context, prerequisites, or exclusions.

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?

Annotations are absent, so the description carries the full burden. It discloses one meaningful behavioral trait: automatic substring fallback ('自动子串回退'), which helps predict matching behavior. However, it does not mention what happens on zero results, output formatting, or rate limits, leaving significant gaps for a tool with no other behavioral documentation.

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, consisting of a single sentence with an illustrative example. Every part adds value, the key capability is front-loaded, and there is zero 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 simple two-parameter search tool, the description is adequate but incomplete. It covers the primary purpose and a key behavioral feature (substring fallback), but it does not mention the output structure, default pagination, or edge-case handling. Given the absence of an output schema, a bit more detail would round it out.

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 is expected to compensate. It provides semantics for the 'keyword' parameter by indicating it is a product name keyword (Chinese), reinforced by the example. However, the 'limit' parameter is completely unexplained, and no default behavior is noted, leaving half the parameters ambiguous.

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 function with a specific verb and resource: search China export rebate rates by product name keyword, with a concrete example. This unambiguously distinguishes it from sibling tools like query_rebate_rate and rebate_rate_stats, which likely serve different querying or statistical purposes.

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?

No explicit guidance is provided on when to use this tool versus the alternative sibling tools. The description implies usage for keyword-based product name searches but does not mention situations where a different tool would be more appropriate, nor does it state any exclusions or prerequisites.

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