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SupplyGraph.AI.Daasmart

Enterprise Change Industry Academia Coop

enterprise_change_industry_academia

基于具体企业名称,按企业查询合作品牌方面的周期变化,用于查询与高校院所的产学研、研发合作及联合实验室。不用于查询高校或科研机构自身信息。 涉及指标/类型:是否与高校或科研机构建立产学研合作;是否与高校或科研机构建立了产学研合作关系;是否与高校或科研机构建立了研发合作;是否与高校或科研机构建立联合实验室 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是否与高校或科研机构建立产学研合作;美国Tesla, Inc.是否与高校或科研机构建立了产学研合作关系;日本丰田自动车株式会社是否与高校或科研机构建立了研发合作

Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 40, "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

A3.6/5.0
Behavior3/5

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

The description lists the specific indicators covered (whether cooperation exists, types of cooperation) and exclusions. The openWorldHint annotation is present, and the description adds context about what's included/excluded. However, it doesn't disclose return format, pagination, or other behavioral details beyond the annotation.

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 well-structured with clear sections: purpose, indicators, exclusions, and examples. It's front-loaded with the main purpose and provides useful examples. Slightly verbose with the indicator list but each item adds value.

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 tool's moderate complexity (2 params, output schema exists), the description covers purpose, scope, exclusions, and examples. The output schema handles return value documentation. The description is complete enough for an agent to select and invoke correctly.

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 description coverage is 100%, so the schema already documents both parameters (company_name and country_name) with examples. The description adds context about the query scope (specific company name) but doesn't add significant new meaning beyond the schema.

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

Purpose4/5

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

The description clearly states the tool queries cooperation brand-related periodic changes for a specific enterprise, focusing on industry-academia-research cooperation, R&D cooperation, and joint laboratories. It distinguishes from sibling tools by specifying it's not for querying university/research institution info and not for batch filtering by park/industry chain.

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?

The description provides explicit usage context: use when querying a specific company's cooperation with universities/research institutions. It lists exclusions (not for university info, not for batch filtering) and gives typical query examples, though it doesn't name alternative tools explicitly.

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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