SupplyGraph.AI Data

Enterprise Change International Influence

enterprise_change_international_influence

基于具体企业名称，按企业查询声誉品牌方面的周期变化，用于查询国际媒体研报热度、信用评级、获奖与舆情健康度。不用于仅限国内的知名度或美誉度指标。涉及指标/类型：国际媒体报道数量；国际机构研报数量；国外网络社交平台热度；国际信用评级；国际获奖数量；国际舆情健康度不包含：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国际媒体报道数量；美国Tesla, Inc.国际机构研报数量；日本丰田自动车株式会社国外网络社交平台热度

Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 60, "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

A4.3/5.0

Behavior4/5

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

With only openWorldHint annotation, the description adds meaningful behavioral context by enumerating included metric categories, excluding non-category indicators, and specifying it queries periodic changes. It does not fully disclose data freshness or pagination, but no annotation contradiction exists.

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 structured with sections for purpose, exclusions, metrics, and examples, making it scannable. It is somewhat long due to the metric enumeration and pricing detail, but each part contributes useful information.

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?

For a 2-parameter tool with an output schema, the description is thorough: it explains scope, boundaries, metrics, and example queries. It does not discuss ambiguity handling or data source limitations, but these are minor gaps given the output schema covers return values.

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?

Input schema covers 100% of parameters, and the description adds value with typical question examples that demonstrate parameter format (country prefix plus company name), reinforcing the schema's short descriptions.

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 periodic reputation/brand changes for a specific company, listing concrete metrics (international media coverage, research reports, social platform heat, credit ratings, awards, public opinion health). It distinguishes from siblings by explicitly excluding domestic-only awareness/favorability and batch filtering by park/industry chain.

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?

Provides explicit when-to-use context (international reputation/brand metrics) and when-not-to-use exclusions (domestic-only metrics, batch filter lists). Does not name specific alternative sibling tools, but the typical question patterns give clear guidance on input formulation.

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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