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SupplyGraph.AI.Daasmart

Enterprise Change Public Responsibility

enterprise_change_public_responsibility

基于具体企业名称,按企业查询责任品牌方面的周期变化,用于查询纳税缴费与创造就业岗位。不用于欠税、非正常纳税户等违规违法排查。 涉及指标/类型:缴纳税额;缴费数额;创造就业岗位 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缴纳税额;美国Tesla, Inc.缴费数额;日本丰田自动车株式会社创造就业岗位

Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 30, "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?

The annotation openWorldHint=true provides minimal behavioral guidance. The description adds that it covers periodic changes in specific responsibility indicators, but does not specify data update frequency, pagination, or whether results are time-series vs. point-in-time. No contradiction with annotations.

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, covered indicators, exclusions, typical queries). It is dense but focused, with no wasted sentences. The pricing metadata is incidental but not part of the description.

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 (querying periodic changes with specific indicators), the description adequately covers purpose, exclusions, and typical usage. The output schema exists, so return-value details are not required. It could benefit from clarifying the temporal scope of 'periodic changes' (e.g., yearly, quarterly), but overall it is complete.

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%, as both parameters (company_name, country_name) have descriptive properties in the schema. The description provides typical query examples ('中国比亚迪股份有限公司缴纳税额; 美国Tesla, Inc.缴费数额') that illustrate parameter usage, adding a small increment of value beyond the schema.

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 responsibility brand aspects by specific company name, explicitly listing the indicator types (tax payment amount, contribution amount, employment creation) and typical query phrasings. It distinguishes from siblings that handle other responsibility aspects (e.g., enterprise_change_charity_responsibility, enterprise_change_env_responsibility) and from tools that handle tax violations (chain_a_taxpayer_company_list) or batch screening.

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

Usage Guidelines5/5

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

Explicitly states what it is used for (querying tax payments and employment creation) and what it is NOT for (non-compliant tax arrears, abnormal taxpayer investigations). Also excludes batch filtering by park/industry chain, providing clear boundaries vs sibling tools like park_a_taxpayer_company_list or chain_a_taxpayer_company_list.

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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