SupplyGraph.AI Data

query_gov_transport_index

gov_data_transport

查询地区交通运行宏观统计指标。覆盖：公交线路与覆盖、高峰通行速度、铁路/民航班次与吞吐量等统计值。不返回地铁站/公交站等具体点位列表与分布（明细请用「地铁站公交站等交通设施分布」poi_data_transport）。典型问法：某区早高峰通行速度、机场吞吐量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「通行速度运量等交通运行指标」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：武侯区早高峰通行速度

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

A4.7/5.0

Behavior4/5

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

Beyond the openWorldHint annotation, the description discloses the query-only nature, the exclusion of detailed POIs, and the pricing model with a clear definition of data units. It does not explicitly state safety (read-only) but implies it via '查询'. The pricing detail adds transparency beyond the minimal annotation.

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 compact but packed with essential information: capability, covered indicators, exclusions, alternative tool, typical queries, and pricing. Each sentence serves a distinct purpose with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (3 parameters, output schema present), the description covers the what, why, when, and pricing. It includes enough context for the agent to decide and invoke correctly, and does not need to repeat return format since output schema exists.

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?

The schema already describes all 3 parameters with coverage at 100%, so baseline is 3. The description adds value by providing typical query examples and clarifying the domain (transport indicators), which helps populate input_text appropriately. It also clarifies the scope of gov_names via the covered indicators, though the schema is already detailed.

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 macro transportation statistics indicators for regions, listing covered categories (bus lines, peak speeds, rail/flight frequency) and explicitly excluding detailed POI locations. It distinguishes itself from the sibling poi_data_transport by providing a precise scope and typical query examples.

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?

The description explicitly mentions when *not* to use this tool (for specific point locations) and directs users to the alternative poi_data_transport. It also gives typical query phrases, helping the agent understand the intended use cases.

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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