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Glama

SupplyGraph.AI Data

by io.github.SupplyGraphAI

Enterprise Change Executive Sentiment

enterprise_change_executive_sentiment

基于具体企业名称，按企业查询舆情方面的周期变化，用于查询高管社交异常、丑闻曝光及负面舆情。不用于高管任职变动查询，也不用于企业主体层面的一般舆情。 涉及指标/类型：高管个人社交账号是否存在异常动态更新；是否有高管被爆出丑闻的相关信息；是否有关于高管的负面舆情 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高管个人社交账号是否存在异常动态更新；美国Tesla, Inc.是否有高管被爆出丑闻的相关信息；日本丰田自动车株式会社是否有关于高管的负面舆情

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_nameYes企业名称，如「比亚迪股份有限公司」「Tesla, Inc.」。
country_nameYes国家名称，如「中国」「美国」「Japan」「China」。

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText 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.

  1. First observed

TDQS

A4.5/5.0
Behavior3/5

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

The description implies a read-only query operation (using '查询') but does not explicitly state that it has no side effects, requires authentication, or has any rate limits. Since annotations only provide openWorldHint and no readOnlyHint, the description carries the burden for behavioral disclosure and falls short of fully specifying the tool's behavior.

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, with clear sections for included indicators, excluded content, and typical questions. It avoids redundancy and directly conveys the necessary information without extraneous details.

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 lack of an output schema, the description sufficiently explains the tool's purpose, scope, and usage context. It covers what the tool does, what it does not do, and provides realistic examples, making it complete enough for an agent to understand when and how to use it.

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 input schema already covers both parameters with descriptive examples, so the baseline is 3. The description adds value by clarifying that the company_name must be specific and that the focus is on executives, and by providing typical queries that illustrate how to combine company_name and country_name.

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 function: querying periodic public opinion changes related to executives, specifically social anomalies, scandals, and negative sentiment. It explicitly distinguishes itself from executive appointment changes and general enterprise-level public opinion, and provides concrete examples of typical queries.

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 gives explicit guidance on when to use this tool by stating what it is not for (executive appointment changes, general enterprise public opinion) and what it includes (specific executive-related indicators). It also lists typical query patterns, making it clear for an agent to decide when to invoke this tool over alternatives.

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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