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SupplyGraph.AI.Daasmart

Business Surrounding Shop

business_surrounding_shop

基于中国范围内的具体地址或地点(如门牌、地标、路名、小区名、园区出入口等,必须是中国境内可定位的具体点,不能是市名或区县名本身;不支持境外地址),查询其附近/周边的店铺统计(返回餐饮/购物/休闲娱乐等大类及子类数量统计,不是店铺名称与坐标明细列表)。 涉及指标/类型:餐饮类店铺数量及子类统计(如中餐、快餐等);购物类店铺数量及子类统计(如衣帽、化妆品等);休闲娱乐类店铺数量及子类统计(如电影院、网吧、KTV等) 不包含:店铺名称与坐标明细列表;企业与个体工商户数量;房价与人口指数 典型问法:北京市朝阳区阜通东大街6号周边有多少餐饮店;成都高新区天府大道中段666号周边购物类店铺统计;苏州工业园区星湖街328号周边休闲娱乐店铺有多少

Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 100, "unit_description": "optional"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityYes中国境内地级市名称(设区的市,如「北京」「成都」「苏州」);只返回中文城市名,不含「市」字后缀(如「北京」而不是「北京市」)。
addressYes中国境内结构化中文地址,按「国家、省份、城市、区县、城镇、乡村、街道、门牌号码、屋邨、大厦」从大到小拼接;须为中国范围内可定位地址,不支持境外地址;缺失层级跳过,顺序不可颠倒。示例:北京市朝阳区阜通东大街6号。

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText or Markdown response with surrounding shop statistics by dining (e.g. Chinese food, fast food), shopping (e.g. clothing, cosmetics), and leisure (e.g. cinema, internet cafe, KTV) categories including subcategory counts. Also used for in-progress, failed, cancelled, or waiting-user messages.

TDQS

A4.5/5.0
Behavior4/5

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

The annotation openWorldHint is set, and the description adds context on what the tool does not cover (e.g., no shop names, no company counts) and specifies the return type (statistics). It does not contradict annotations, but since openWorldHint is broad, the description provides useful boundary information.

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 detailed but not overly long. It front-loads the purpose and includes examples, though the pricing note is somewhat unusual. Overall, it is structured clearly with sections for metrics, exclusions, and typical queries.

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 complexity (statistics queries for shops in China), the description covers key aspects: input constraints, metrics, exclusions, and example use cases. The presence of an output schema reduces the need to describe return values. The description is sufficiently complete for an agent to use it correctly.

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?

While the input schema already provides descriptions for both parameters (city and address), the tool description adds context on the expected format (e.g., Chinese address structure) and constraints (no city names alone). Given the schema coverage is 100%, the extra guidance in the description is a bonus, not a necessity.

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 explicitly states the tool queries the surroundings of a specific address in China for shop statistics, listing major categories and subcategories (dining, shopping, entertainment). It clearly differentiates from siblings like business_surrounding_company by specifying it returns counts, not lists.

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?

It provides explicit usage guidelines, including typical queries, constraints (China only, no city-level addresses), and what is excluded (shop names, coordinates). It also distinguishes from sibling tools by stating it returns counts not lists.

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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