SupplyGraph.AI Data

Enterprise Change Gov Visit Exchange

enterprise_change_gov_visit_exchange

基于具体企业名称，按企业查询政企关系方面的周期变化，用于查询接待政府视察与出访外地政府机构等互动。不用于补贴资助或税收优惠等财政支持查询。涉及指标/类型：是否接待过本地政府领导的视察；是否接待过来自其他地区的政府领导；是否访问过外地政府机构不包含：非本分类指标；按园区/产业链批量筛企业名单典型问法：中国比亚迪股份有限公司是否接待过本地政府领导的视察；美国Tesla, Inc.是否接待过来自其他地区的政府领导；日本丰田自动车株式会社是否访问过外地政府机构

Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 30, "unit_description": "optional"}

Input Schema

TableJSON Schema

Name	Required	Description	Default
`company_name`	Yes	企业名称，如「比亚迪股份有限公司」「Tesla, Inc.」。
`country_name`	Yes	国家名称，如「中国」「美国」「Japan」「China」。

Output Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes	Text or Markdown response generated by the agent. Returned for completed results as well as in-progress, failed, cancelled, or waiting-user messages.

Schema Changelog

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

First observedAug 19, 2026

TDQS

A4/5.0

Behavior3/5

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

The description adds useful context beyond the openWorldHint annotation by specifying the exact indicators covered (local government visits, external government visits, etc.). However, it does not disclose potential limitations such as data freshness, pagination, or any side effects, leaving a moderate gap in behavioral disclosure.

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 well-structured with clear sections: main purpose, exclusions, included indicators, and example queries. It is concise enough for an agent to parse quickly, though the examples could be slightly trimmed without losing meaning.

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 presence of an output schema and the tool's moderate complexity, the description is comprehensive: it covers purpose, scope, exclusions, and usage examples. The typical questions provide practical guidance, and no critical operational details are missing.

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 coverage is 100% for the two parameters, so the schema already defines company_name and country_name clearly. The description adds value through typical usage examples showing format variations (e.g., Chinese and English country names), but this is incremental rather than essential, justifying the baseline score of 3.

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's purpose: querying enterprise-government relationship periodic changes based on a specific enterprise name, specifically for interactions like receiving government inspections and visiting external government agencies. It lists concrete indicators and explicitly excludes fiscal support queries, distinguishing it from siblings like enterprise_change_policy_fiscal_support.

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 provides clear exclusions (not for subsidies/tax benefits, not for batch screening by park/chain) and gives typical question examples. While it does not name specific alternative tools, the when-not guidance is explicit and helps an agent choose this tool over other enterprise_change_* or list tools.

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).

Resources

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