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SupplyGraph.AI.Daasmart

Enterprise Change Credit Debt Risk

enterprise_change_credit_debt_risk

基于具体企业名称,按企业查询企业风险方面的周期变化,用于查询债务违约、负债概况、征信不良与账户冻结等。不用于替代完整征信报告,也不与欠税失信等违规违法记录混用。 涉及指标/类型:企业是否存在未按期偿还的重大债务违约;当前企业负债总额及违约情况如何;企业征信报告中是否有不良信用记录;是否存在被冻结的银行账户 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企业是否存在未按期偿还的重大债务违约;美国Tesla, Inc.当前企业负债总额及违约情况如何;日本丰田自动车株式会社企业征信报告中是否有不良信用记录

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

A3.9/5.0
Behavior3/5

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

Annotations include openWorldHint: true, which the description does not contradict. The description adds scope details (types of indicators covered) and mentions 'periodic changes' but does not describe any side effects, rate limits, or auth needs. Given the tool is a read-only query, the additional context is moderate.

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: it opens with the primary purpose, lists exclusions, details specific indicators, then provides typical questions. It is informative without being verbose, earning its place in each sentence.

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 an output schema exists (though not shown), the description focuses on scope and exclusions, and provides practical examples. It sufficiently covers the tool's purpose and boundaries for an agent to decide when to invoke it.

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 coverage is 100% and both parameters have clear descriptions. The description adds typical examples of company names and countries, reinforcing usage, but does not elaborate beyond the schema. Baseline 3 is appropriate.

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 periodic changes in enterprise risk aspects related to debt defaults, liabilities, credit records, and frozen accounts. It specifically identifies the resource (company) and the risk categories, distinguishing it from other enterprise_change_* siblings by its focus on credit/debt risk.

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 usage context by stating it queries by specific company name, and explicitly lists exclusions (not for full credit reports, not for tax violations, and not for batch screening). Typical questions are given, which clarify appropriate usage. It does not name alternative tools but the boundaries are clear.

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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