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SupplyGraph.AI.Daasmart

Enterprise Change Innovation

enterprise_change_innovation

基于具体企业名称,按企业查询经营活动方面的周期变化,用于查询专利与软著等创新成果数量。不用于研发投入、线上销售占比等创新力指标,也不提供专利全文。 涉及指标/类型:目前持有的有效专利总数是多少;目前已获得授权的发明专利数量是多少;目前公布的发明专利中,有多少是有效的;目前持有的实用新型专利数量是多少;目前持有的外观设计专利数量是多少;目前拥有的软件著作权数量是多少 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目前持有的有效专利总数是多少;美国Tesla, Inc.目前已获得授权的发明专利数量是多少;日本丰田自动车株式会社目前公布的发明专利中,有多少是有效的

Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 60, "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

A4.1/5.0
Behavior3/5

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

Annotations only include openWorldHint=true, so the description carries the transparency burden for behavior. It adds useful limitations (no patent full text, not for R&D/online-sales indicators) and indicates a read-style query, but it does not describe response behavior, historical-vs-current semantics, pagination, or other operational traits.

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

Conciseness4/5

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

The description is organized into purpose, exclusions, metric list, non-inclusions, and example questions. It is longer than strictly necessary and includes a pricing JSON block, but every section adds useful selection or invocation context with clear separation.

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 two simple parameters, an output schema, and a large sibling set, the description covers purpose, exclusions, indicator scope, and example phrasings. It does not need to explain return structure because an output schema exists, though naming direct alternatives would improve completeness.

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

Parameters4/5

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

Schema coverage is 100% for both parameters, so baseline is 3. The description adds value by giving concrete example values for company_name and country_name, including Chinese and English country forms, and reinforces that the query is by specific enterprise name rather than batch filtering.

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?

Description states a specific verb and resource: query by specific enterprise name for periodic changes in business activities, specifically counting patents and software copyrights. It explicitly distinguishes from sibling tools by excluding other innovation metrics like R&D investment/online sales ratio and by excluding batch park/industry-chain screening.

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?

Clear usage context is provided: use for patent and software copyright counts by specific enterprise, not for other innovation-force indicators, not for patent full text, and not for batch screening. However, it does not name alternatives or explicitly say 'use X instead', so it stops short of full alternative guidance.

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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