Skip to main content
Glama

Enterprise Change Executive Change

enterprise_change_executive_change

基于具体企业名称,按企业查询人事变动方面的周期变化,用于查询核心管理层变动、高管离职与调岗。不用于高管负面舆情或丑闻查询。 涉及指标/类型:核心管理层是否发生人事变动;是否有高管离职;是否有高管调岗 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核心管理层是否发生人事变动;美国Tesla, Inc.是否有高管离职;日本丰田自动车株式会社是否有高管调岗

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
Behavior4/5

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

No readOnly/destructive annotations are present, so the description carries the burden. It uses '查询' (query) implying read-only behavior and specifies exclusions, but does not mention potential side effects or limitations like rate limits or data freshness.

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 moderately long but well-structured, with purpose, exclusions, and examples. It is not overly verbose and every sentence adds value.

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

Completeness3/5

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

There is no output schema, and the description does not explain what the return data looks like (e.g., a list of changes, boolean, details). This leaves some ambiguity about the expected result format.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Both parameters (company_name, country_name) have clear descriptions with example values, and the tool description reiterates them with concrete examples. Schema coverage is 100% and the description enhances understanding.

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?

Description clearly states it queries executive changes (core management, departures, transfers) for a specific company, and explicitly excludes negative public opinion or scandal queries. This distinguishes it from related tools.

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?

Provides explicit when-to-use (querying personnel changes for a named company) and when-not-to-use (not for scandals, not for batch screening by park/industry chain) guidance, along with concrete example questions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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