Skip to main content
Glama

Enterprise Change Human Resources

enterprise_change_human_resources

基于具体企业名称,按企业查询经营活动方面的周期变化,用于查询新员工与异地招聘,以及研发销售管理层岗位招聘。不用于薪酬福利、离职晋升等雇主品牌类指标。 涉及指标/类型:是否进行了新员工的招聘;是否在其他地区进行过员工招聘;是否招聘了研发或技术类的职位;是否招聘了销售或市场类的职位;是否进行了管理层级别的职位招聘 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进行了新员工的招聘;美国Tesla, Inc.是否在其他地区进行过员工招聘;日本丰田自动车株式会社是否招聘了研发或技术类的职位

Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 50, "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

A4.3/5.0
Behavior4/5

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

Annotations only include openWorldHint, so the description carries the burden of disclosing behavioral scope. It enumerates the exact indicators and explicitly mentions what is excluded, which adds valuable context. However, it does not discuss data freshness or time-window granularity, though the output schema likely covers return structure.

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 and front-loaded with the main purpose, followed by indicator lists, exclusions, and examples. It is slightly repetitive between the indicator list and typical questions, but this is justified given the need to disambiguate from many closely related sibling tools.

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 tool with two simple parameters and an existing output schema, the description thoroughly covers scope, exclusions, and usage examples. It could clarify the meaning of 'periodic changes' (e.g., time range), but overall it is sufficiently complete for correct selection and invocation.

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?

The input schema already provides 100% coverage with descriptions and examples for both country_name and company_name. The tool description reinforces these with typical questions but does not add new semantic meaning beyond what the schema offers.

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 queries periodic changes in business activities for HR-related topics such as new employee recruitment, off-site recruitment, and R&D/sales/management positions. It explicitly lists the indicators covered and distinguishes itself from sibling tools by specifying exclusions like salary/benefits and batch screening 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 Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states what the tool is used for ('用于...') and what it is not used for ('不用于...' and '不包含...'), providing clear when-to-use guidance. It also includes typical question patterns, making it easy for an agent to match the tool to user intent.

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