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Glama

query_poi_automotive_list

poi_data_automotive

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汽车与摩托相关 POI 明细列表。覆盖:加油站、充电站、4S店、汽修洗车、摩托车服务等。不回答加油站数量等统计(请用兴趣点数量指标)。典型问法:某区加油站分布、充电站列表、洗车场有哪些。

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「加油站充电站汽修等汽车服务分布」POI 明细/分布意图;须指向单一地区(地级市或区县)。示例:武侯区的加油站分布

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Although annotations only include openWorldHint, the description discloses key behavioral constraints: requires a single clear administrative region name, does not answer statistical queries, and counts one data unit per region×POI type. It does not contradict the annotation and provides practical limitations beyond a simple read operation, though it does not detail return format or pagination.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise and well-structured: first sentence states purpose and scope, second covers exclusions, third provides typical queries, and pricing is clearly appended. No redundant sentences; every part earns its place.

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 (4 params, output schema exists), the description covers the essential context: what it does, what it doesn't do, usage examples, and pricing model. It lacks explicit mention of return format, but the presence of an output schema obviates that need. Overall adequate 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 descriptions cover 100% of the four parameters, each with meaningful explanations (e.g., gov_name is single region, poi_type for disambiguation, input_text describes intent). The tool description adds no extra semantic value beyond the schema, so 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?

Description clearly states the tool queries automotive-related POI details for a specific administrative region, listing covered categories (gas stations, charging stations, 4S shops, car repair/wash, motorcycle services). It distinguishes from sibling POI tools by specifying the automotive scope, so purpose is explicit and distinct.

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 explicitly says not to use this tool for statistical counts like number of gas stations, directing to a different metric. It also states that multiple regions are not supported and gives typical query examples, providing clear context for when to use. However, it does not explicitly name alternative tools, though the exclusion guidance is valuable.

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

B3.2/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with consistent scopes (e.g., chain_* vs park_* vs company_* vs gov_data_*). The list/num pairs are clearly differentiated. A few overlapping concepts exist (e.g., company_patent vs enterprise_change_innovation) but descriptions clarify the angle. Some typos (company_randomin_spection) don't cause ambiguity.

Naming Consistency4/5

Naming follows a mostly predictable snake_case pattern with prefixes indicating domain (chain_, park_, company_, enterprise_change_, gov_data_, poi_data_, business_surrounding_, cbd_surrounding_). Most tools use <prefix>_<entity>_<action> or <prefix>_<subject>. A few outliers (sg_chokepoint, tariff_calc, corporate_exception_report) deviate but are few and recognizable.

Tool Count1/5

With 198 tools, this is far beyond any reasonable scope for a single server. It exceeds even the 'extreme mismatch' threshold of 50+ tools. The large number makes selection and discoverability challenging, despite good internal organization.

Completeness4/5

The tool surface covers a vast range of enterprise data, regional macro stats, POI details, supply chain analysis, and tariffs. It appears to cover the primary domain comprehensively, with only minor potential gaps (e.g., no direct tool for company debt ratings or specific product catalogs, but these are addressed via enterprise_change_* and company_* tools).

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