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SupplyGraph.AI.Daasmart

Enterprise Change Capital Brand Innovation

enterprise_change_capital_brand_innovation

基于具体企业名称,按企业查询资本品牌方面的周期变化,用于查询研发投入、专利申报、线上销售占比与管理数字化程度。不用于专利软著持有件数等创新成果数量统计。 涉及指标/类型:研发投入额度;研发投入营收占比;专利申报数量;企业销售线上占比;企业管理数字化程度 不包含:非本分类指标;按园区/产业链批量筛企业名单 典型问法:中国比亚迪股份有限公司研发投入额度;美国Tesla, Inc.研发投入营收占比;日本丰田自动车株式会社专利申报数量

Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 50, "unit_description": "optional"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_nameYes企业名称,如「比亚迪股份有限公司」「Tesla, Inc.」。
country_nameYes国家名称,如「中国」「美国」「Japan」「China」。

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText or Markdown response generated by the agent. Returned for completed results as well as in-progress, failed, cancelled, or waiting-user messages.

TDQS

A3.8/5.0
Behavior2/5

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

Annotations only include openWorldHint (minimal information). The description carries full burden for behavioral disclosure but is silent on side effects, read-only nature, data completeness, or any special handling. It does not contradict annotations but fails to add transparency beyond the openWorldHint.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections: purpose, indicators, exclusions, and examples. It is concise (no redundant information) and front-loads the primary purpose, making it easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists (not shown but indicated) and parameters are well-described, the description covers purpose, scope, exclusions, and typical usage. It is complete for agent decision-making, though it could optionally mention data sources or time periods, but these are not required for basic tool selection.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema descriptions already cover both parameters with examples (company_name, country_name). The description adds typical query formats and lists indicators, but these are not parameter-specific refinements. With 100% schema coverage, the baseline is 3, and the description offers marginal value beyond it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool queries capital brand related periodic changes for a specific enterprise, enumerates the exact indicators (R&D investment, patent applications, online sales ratio, digitalization degree), and explicitly excludes innovation output counts. This differentiates it from sibling tools like enterprise_change_innovation and chain_have_patent_company_list.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides explicit exclusions (not for patent/copyright counts, not for batch filtering by park/industry chain) and typical query examples, which guide when to use. It does not explicitly name alternative tools, but the exclusions effectively delineate usage boundaries.

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

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TDQS

B3/5.0
Disambiguation2/5

大量工具功能高度重叠,例如chain_*和park_*系列均为按不同筛选条件查询企业列表或数量,只是参数不同却拆分为独立工具;enterprise_change_*系列同样针对不同指标逐一拆分。虽然描述清楚各自区别,但代理面对198个工具时极易选错,且许多工具本质应合并为带参数的单一接口。

Naming Consistency3/5

多数工具采用snake_case加领域前缀(如chain_、park_、company_、gov_data_、poi_data_),但存在明显变体如company_certlist、company_randomin_spection(拼写异常)、corporate_exception_report、due_diligence_report、sg_chokepoint等,混用英文抽象名词与动词短语,整体模式可辨认但不统一。

Tool Count1/5

工具总数高达198个,远超合理范围(即使复杂领域也应控制在25个以内)。大量工具是同一逻辑的不同参数变体(如list/num、不同资质条件),完全可以通过参数化减少数量,严重冗余,代理难以有效浏览和选择。

Completeness4/5

工具覆盖领域广泛,包括企业信息、产业链分析、园区统计、地区宏观、POI明细、供应链风险、关税计算等,基本覆盖了商业数据查询的主要需求。虽缺少更新/删除等操作(但作为查询服务器可接受),且部分细分领域可能有遗漏,但整体功能较为完整。

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