SupplyGraph.AI Data

query_gov_population_index

gov_data_population

查询地区人口宏观统计指标（数量/占比/增长率）。覆盖：常住与户籍人口、年龄代际人口（60/70/80/90后等统称人口数量与占比）、劳动力/老年/儿童人口、城镇化率、人口增长。不回答企业数量或 POI 门店明细。典型问法：某区常住人口、劳动力人口占比、80后人口数量、人口TOP城市。

Pricing: {"unit": "credits", "billing_model": "per_data_unit", "meter": {"credits_per_unit": 1, "unit_description": "One data unit = one region × one indicator × one date version (example: Chengdu × permanent population × 2023). Charged by returned units after query, capped by the user request."}}

Input Schema

TableJSON Schema

Name	Required	Description
`versions`	No	可选期望年份/日期软约束，如 ['2022'] 或 ['2022-12-01']；取数以库内真实版本为准，不一致时标注 version_mismatch。
`gov_names`	No	可选地区名列表。point/compare：目标地区；rank/list/filter：父级范围（如 ['四川省']/'成都市'）；peer_rank：目标地区（可另附上级）；不传时尝试从 input_text 抽取。
`input_text`	Yes	用户查询文本，描述「人口数量与结构指标」指标意图；支持点查、TOP/排名、多地对比、下级列表、阈值筛选、同级位次等。示例：武侯区常住人口有多少

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

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

First observedAug 19, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations only include openWorldHint: true, so the description carries the burden. It includes pricing information (credits per data unit) and describes coverage and exclusions. It does not describe side effects or auth requirements, but it adds valuable context about cost and scope. 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.

Conciseness5/5

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

The description is concise and well-structured, opening with the primary purpose, followed by covered indicators, explicit exclusions, and typical queries. The pricing block is appended as extra but relevant info. Every sentence adds value without redundancy.

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 has an output schema (signaled), the description adequately covers when to use, what it covers, and pricing. It mentions version handling indirectly in the versions param description. It does not address edge cases like no-data responses, but overall it is sufficient for an agent to correctly invoke and understand the tool.

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 parameters with detailed explanations for each (versions, gov_names, input_text). The main description adds example queries but does not provide additional semantics beyond the schema. Baseline is 3 due to full schema coverage, and description does not meaningfully enhance parameter understanding.

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 it queries regional population macro statistics (counts, proportions, growth rates) and lists specific indicator categories (permanent/resident population, age cohorts, labor/elderly/children, urbanization rate, population growth). It also explicitly excludes enterprise counts and POI details, distinguishing it from sibling gov_data_* tools. Example queries are provided.

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 context on when to use (population-related queries) and explicitly states what it does not answer (enterprise counts or POI details). It also provides typical query phrasing. However, it does not name alternative tools for excluded topics, but the exclusions themselves guide usage implicitly.

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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