SupplyGraph.AI Data

Enterprise Change Env Responsibility

enterprise_change_env_responsibility

基于具体企业名称，按企业查询责任品牌方面的周期变化，用于查询绿色投入、能耗碳排放及环保处罚罚款。不用于是否通过环保/ISO等认证查询。涉及指标/类型：绿色投入总额；节能额度；碳排放总量；人均能耗；综合产值能耗；环保监管处罚次数；监管罚款金额不包含：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绿色投入总额；美国Tesla, Inc.节能额度；日本丰田自动车株式会社碳排放总量

Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 70, "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?

The description presents this as a query-oriented operation, lists the exact metrics covered, discloses per-run pricing, and states exclusions; since annotations only provide openWorldHint and no readOnly/destructive hints, this extra context is valuable. It stops short of clarifying time granularity or unmatched-company behavior.

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 front-loaded with purpose and uses clear lists for indicators, exclusions, and examples without excessive prose. The embedded Pricing JSON is somewhat structural noise, but the overall structure remains scannable and efficient.

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 two-parameter tool with an output schema and many sibling tools, the description supplies the essential selection cues: metrics, exclusions, examples, and cost. The main gap is the lack of explicit definition for '周期变化' time window or granularity, though the output schema may address this.

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?

Schema coverage is 100%, and the schema already describes both parameters, but the description adds meaning via '按企业查询' and typical questions like '中国比亚迪股份有限公司绿色投入总额' that demonstrate valid country/company pairings and implied exact-name usage.

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 opens with a specific verb and resource: '基于具体企业名称，按企业查询责任品牌方面的周期变化' and then enumerates exact metrics such as 绿色投入总额, 碳排放总量, and 环保处罚罚款. It also explicitly excludes certification queries, which distinguishes it from sibling tools like enterprise_change_certification.

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 explicit when-not-to-use guidance: '不用于是否通过环保/ISO等认证查询' and '不包含：非本分类指标；按园区/产业链批量筛企业名单'. It does not name specific alternative sibling tools, but the exclusions are clear enough to prevent common misuse.

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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