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SupplyGraph.AI.Daasmart

query_poi_medical_list

poi_data_medical

基于明确的市级或区县级行政区名称,查询该行政区范围内的医疗保健 POI 明细列表。覆盖:综合医院、三甲医院、专科医院、卫生院、药店等。不回答医院/药店数量(请用兴趣点数量指标)。典型问法:某区综合医院分布、有哪些三甲医院、药店列表。

Pricing: {"unit": "credits", "billing_model": "per_data_unit", "meter": {"credits_per_unit": 1, "unit_description": "One data unit = one region × one POI type (example: Wuhou District × metro station). Not charged per POI store/row."}}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
versionNo可选数据版本,如 '2022' 或 '2022-12-01';不传则使用库内默认/最近可用版本。
gov_nameNo可选,单一地区名(地级市或区县)。不支持同时查多个地区;不传时从 input_text 抽取。
poi_typeNo可选,POI 类型名或编码(如 地铁站 / 150500);用于消歧;不传时从 input_text 识别,且限定在本主题候选集内。
input_textYes用户查询文本,描述「医院诊所药店等医疗点分布」POI 明细/分布意图;须指向单一地区(地级市或区县)。示例:武侯区的综合医院分布

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.1/5.0
Behavior4/5

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

With only openWorldHint annotation present, the description carries the behavioral disclosure burden. It adds meaningful constraints: requires a single clear administrative region, covers specific medical POI categories, does not answer counts, and explains pricing is per region×type rather than per row. This goes beyond the annotation without contradicting it.

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 reasonably concise and front-loaded: function, coverage, exclusions, examples, then pricing. The pricing JSON adds useful cost semantics and is not wasteful, though it slightly increases length.

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 a present output schema, 100% schema parameter coverage, and the tool's relatively simple list-query nature, the description is complete enough. It covers scope, exclusions, typical usage, and billing model. It could name direct alternatives more explicitly, but the sibling list and tool name provide sufficient context.

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 baseline is 3. The description reinforces tool-level intent and gives typical phrasings, but it does not add significant parameter-specific meaning beyond what the schema already provides for input_text, gov_name, poi_type, and version.

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 identifies the verb and resource: '查询该行政区范围内的医疗保健 POI 明细列表' (query medical/healthcare POI detail lists within an administrative region). It also distinguishes itself from count-style sibling tools by explicitly stating it does not answer counts and by listing covered POI subtypes.

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 gives clear usage context: requires a municipal or district-level administrative region and is intended for medical POI list/detail queries. It also provides an explicit exclusion ('不回答医院/药店数量') and directs count-related needs to a different metric, though it does not name alternative sibling 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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