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Glama

Enterprise Change Employee Development

enterprise_change_employee_development

基于具体企业名称,按企业查询雇主品牌方面的周期变化,用于查询员工培训投入时长、晋升率与离职率。不用于是否开展招聘,也不用于满意度敬业度等评价结果。 涉及指标/类型:员工平均培训投入;员工平均培训时间;员工晋升率;员工离职率 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员工平均培训投入;美国Tesla, Inc.员工平均培训时间;日本丰田自动车株式会社员工晋升率

Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 40, "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

A4.2/5.0
Behavior4/5

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

With only openWorldHint in annotations, the description carries most behavioral disclosure. It adds that this is a read-only query ('查询'), that results are periodic/change-oriented ('周期变化'), and that it costs 40 credits/run. It does not detail period granularity or missing-company behavior, but these are minor gaps.

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 front-loaded: purpose, scope, exclusions, and examples appear in a compact, structured format. No sentence is wasted, and the typical queries materially help invocation.

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?

Given the large sibling family and an output schema, the description covers purpose, metrics, exclusions, examples, and pricing. It does not explicitly specify a time period, but '周期变化' plus the output schema make the tool sufficiently selectable and invokable.

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?

Schema description coverage is 100%, so the schema already documents both parameters with examples. The description reinforces the pairing of country/company with metrics via typical queries, but adds no semantic detail beyond the schema.

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 states a specific action ('按企业查询雇主品牌方面的周期变化') and enumerates exact metrics: training investment/time, promotion rate, and turnover rate. It also distinguishes itself from adjacent tools by explicitly excluding recruiting and satisfaction/engagement evaluations.

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?

It provides clear when-to-use context ('用于查询员工培训投入时长、晋升率与离职率') and when-not-to-use ('不用于是否开展招聘...不包含非本分类指标;按园区/产业链批量筛企业名单'). However, it does not name alternative sibling tools, so an agent must infer which sibling handles the exclusions.

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