server-employee
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_employment_by_sectorA | 특정 국가와 연도의 섹터별 고용 데이터를 조회합니다. Services, Agriculture, Industry 세 섹터의 고용 비율(%)을 반환합니다. Args: country: ISO 2- or 3-letter code (e.g., 'US', 'KR', 'IN') year: Four-digit year (>=1960) Returns: 섹터별 고용 비율 정보. 데이터가 없으면 메시지 반환. |
| get_agriculture_employmentA | 특정 국가와 연도의 농업 섹터 고용 비율을 조회합니다. Args: country: ISO 2- or 3-letter code (e.g., 'US', 'KR', 'IN') year: Four-digit year (>=1960) Returns: 농업 섹터 고용 비율 (% of total employment) |
| get_industry_employmentA | 특정 국가와 연도의 산업 섹터 고용 비율을 조회합니다. Args: country: ISO 2- or 3-letter code (e.g., 'US', 'KR', 'IN') year: Four-digit year (>=1960) Returns: 산업 섹터 고용 비율 (% of total employment) |
| get_services_employmentA | 특정 국가와 연도의 서비스 섹터 고용 비율을 조회합니다. Args: country: ISO 2- or 3-letter code (e.g., 'US', 'KR', 'IN') year: Four-digit year (>=1960) Returns: 서비스 섹터 고용 비율 (% of total employment) |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
The three sector-specific tools are clearly distinct (agriculture, industry, services), but get_employment_by_sector overlaps by returning all three sectors at once. This creates minor ambiguity about which tool to call when only one sector is needed, though the descriptions clearly indicate the granularity difference.
All tools follow a consistent get_<something>_employment pattern with snake_case. The combined tool uses get_employment_by_sector, while the others are get_agriculture_employment, get_industry_employment, get_services_employment, all following a predictable verb_noun structure.
Four tools is well-scoped for a server providing sectoral employment data. Each tool serves a clear role: one combined query and three specific sector queries. The count feels appropriate, not excessive or thin.
The server covers the full domain of sectoral employment ratios: agriculture, industry, and services, plus a combined view. There are no obvious missing operations for a read-only data source; it provides all sector breakdowns available.