China & APAC salary benchmarks (319k+ jobs), statutory cost (五险一金), and employer intelligence.
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Calculate total all-in employer headcount cost in China including mandatory statutory social security (五险一金 - Wuxian Yijin: Pension 16%, Medical 6-10%, Housing Fund 5-12%, Unemployment 0.5%, Work Injury 0.2%) with exact municipal floors (60% avg wage) and ceilings (300% avg wage) across 20+ Chinese cities. Accepts an explicit monthly salary or automatically queries market median P50 for a given role and city.
Target Chinese city (e.g. "Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Chengdu", "Wuhan")
Standardized role (e.g. "Software Engineer", "Java", "Algorithm Engineer") to look up market median salary if monthly_salary_cny is omitted.
Number of employees/headcount to budget for (default: 1)
Seniority level (e.g. "Senior (5-10年)", "Mid (3-5年)") when looking up role.
Annual bonus structure in total months of pay (default: 14 for China tech standard 12 base + 2 bonus)
Employer housing fund contribution rate (e.g. 0.07 for 7%, 0.12 for 12%). Defaults to municipal tech standard.
Gross pre-tax monthly base salary in CNY. If omitted, "role" must be provided.
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Authorization (required): Bearer API token from https://intelligence.teamedupchina.com/dashboard/api-keys (format: Bearer YOUR_API_KEY)Calculate total all-in employer headcount cost in China including mandatory statutory social security (五险一金 - Wuxian Yijin: Pension 16%, Medical 6-10%, Housing Fund 5-12%, Unemployment 0.5%, Work Injury 0.2%) with exact municipal floors (60% avg wage) and ceilings (300% avg wage) across 20+ Chinese cities. Accepts an explicit monthly salary or automatically queries market median P50 for a given role and city.
| Argument | Type | Description |
|---|---|---|
| city* | string | Target Chinese city (e.g. "Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Chengdu", "Wuhan") |
| role | string | Standardized role (e.g. "Software Engineer", "Java", "Algorithm Engineer") to look up market median salary if monthly_salary_cny is omitted. |
| headcount | integer | Number of employees/headcount to budget for (default: 1) |
| seniority | string | Seniority level (e.g. "Senior (5-10年)", "Mid (3-5年)") when looking up role. |
| bonus_months | integer | Annual bonus structure in total months of pay (default: 14 for China tech standard 12 base + 2 bonus) |
| housing_fund_rate | number | Employer housing fund contribution rate (e.g. 0.07 for 7%, 0.12 for 12%). Defaults to municipal tech standard. |
| monthly_salary_cny | number | Gross pre-tax monthly base salary in CNY. If omitted, "role" must be provided. |
{
"type": "object",
"required": [
"city"
],
"properties": {
"city": {
"type": "string",
"description": "Target Chinese city (e.g. \"Shanghai\", \"Beijing\", \"Shenzhen\", \"Hangzhou\", \"Chengdu\", \"Wuhan\")"
},
"role": {
"type": "string",
"description": "Standardized role (e.g. \"Software Engineer\", \"Java\", \"Algorithm Engineer\") to look up market median salary if monthly_salary_cny is omitted."
},
"headcount": {
"type": "integer",
"description": "Number of employees/headcount to budget for (default: 1)"
},
"seniority": {
"type": "string",
"description": "Seniority level (e.g. \"Senior (5-10年)\", \"Mid (3-5年)\") when looking up role."
},
"bonus_months": {
"type": "integer",
"description": "Annual bonus structure in total months of pay (default: 14 for China tech standard 12 base + 2 bonus)"
},
"housing_fund_rate": {
"type": "number",
"description": "Employer housing fund contribution rate (e.g. 0.07 for 7%, 0.12 for 12%). Defaults to municipal tech standard."
},
"monthly_salary_cny": {
"type": "number",
"description": "Gross pre-tax monthly base salary in CNY. If omitted, \"role\" must be provided."
}
}
}Calculate cross-city compensation arbitrage, payroll savings, and engineering runway expansion between Chinese Tier-1 hubs (Shanghai, Beijing, Shenzhen, Guangzhou) and New Tier-1 expansion hubs (Chengdu, Wuhan, Xi'an, Hefei, Suzhou, Hangzhou, etc.). Computes all-in statutory overhead (五险一金), net annual cash savings in CNY & USD, and equivalent headcount bandwidth.
| Argument | Type | Description |
|---|---|---|
| role* | string | Standardized role to compare (e.g. "Software Engineer", "Java Engineer", "Algorithm Engineer", "Product Manager") |
| headcount | integer | Team size / headcount to model (default: 1) |
| seniority | string | Optional seniority slice (e.g. "Senior (5-10年)", "Mid (3-5年)", "Junior (1-3年)") |
| target_city* | string | Expansion/target city (e.g. "Chengdu", "Wuhan", "Xi'an", "Hefei", "Suzhou") |
| bonus_months | integer | Annual compensation months multiplier (default: 14 for China tech standard) |
| baseline_city* | string | Baseline Tier-1 city (e.g. "Shanghai", "Beijing", "Shenzhen", "Guangzhou") |
{
"type": "object",
"required": [
"role",
"baseline_city",
"target_city"
],
"properties": {
"role": {
"type": "string",
"description": "Standardized role to compare (e.g. \"Software Engineer\", \"Java Engineer\", \"Algorithm Engineer\", \"Product Manager\")"
},
"headcount": {
"type": "integer",
"description": "Team size / headcount to model (default: 1)"
},
"seniority": {
"type": "string",
"description": "Optional seniority slice (e.g. \"Senior (5-10年)\", \"Mid (3-5年)\", \"Junior (1-3年)\")"
},
"target_city": {
"type": "string",
"description": "Expansion/target city (e.g. \"Chengdu\", \"Wuhan\", \"Xi'an\", \"Hefei\", \"Suzhou\")"
},
"bonus_months": {
"type": "integer",
"description": "Annual compensation months multiplier (default: 14 for China tech standard)"
},
"baseline_city": {
"type": "string",
"description": "Baseline Tier-1 city (e.g. \"Shanghai\", \"Beijing\", \"Shenzhen\", \"Guangzhou\")"
}
}
}Retrieve detailed firmographic data on a specific Chinese employer. Includes industry, size tier, ownership type, macro category, and employer brand stats. Use for competitive analysis and compensation benchmarking by employer.
| Argument | Type | Description |
|---|---|---|
| name | string | Company name (English or Chinese), supports partial match |
| company_id | integer | Exact company ID |
{
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "Company name (English or Chinese), supports partial match"
},
"company_id": {
"type": "integer",
"description": "Exact company ID"
}
}
}Retrieve paginated historical job postings and salary bands for a specific company by its ID.
| Argument | Type | Description |
|---|---|---|
| cursor | string | Pagination cursor returned from a previous request (optional) |
| per_page | integer | Number of results per page (default: 50, max: 100) |
| company_id* | integer | The ID of the company to retrieve jobs for |
{
"type": "object",
"required": [
"company_id"
],
"properties": {
"cursor": {
"type": "string",
"description": "Pagination cursor returned from a previous request (optional)"
},
"per_page": {
"type": "integer",
"description": "Number of results per page (default: 50, max: 100)"
},
"company_id": {
"type": "integer",
"description": "The ID of the company to retrieve jobs for"
}
}
}Retrieve weekly hiring frequency scores per company/role/city. Shows share-of-voice signals indicating which companies are most actively hiring for which roles. NOT absolute market hiring volume — scores represent frequency within our ingested dataset.
| Argument | Type | Description |
|---|---|---|
| city | string | City filter |
| role* | string | Role to analyze |
| week | string | Calculated week in YYYY-WW format. Defaults to latest. |
| limit | integer | Max results (default 20) |
| company_id | integer | Specific company |
{
"type": "object",
"required": [
"role"
],
"properties": {
"city": {
"type": "string",
"description": "City filter"
},
"role": {
"type": "string",
"description": "Role to analyze"
},
"week": {
"type": "string",
"description": "Calculated week in YYYY-WW format. Defaults to latest."
},
"limit": {
"type": "integer",
"description": "Max results (default 20)"
},
"company_id": {
"type": "integer",
"description": "Specific company"
}
}
}Query aggregated market salary percentiles for a given role, city, seniority, and industry. Returns p25, p50, p75, p90 salary bands with data point counts and confidence levels. Use this to benchmark compensation for specific roles in Chinese and APAC tech labor markets.
| Argument | Type | Description |
|---|---|---|
| city* | string | Target city (e.g. "Beijing", "Shanghai", "Shenzhen", "Taipei") |
| role* | string | Standardized role (e.g. "Software Engineer", "Java Engineer", "Algorithm Engineer") |
| month | string | Calculated month in YYYY-MM format. Defaults to latest available. |
| region | string | Region scope: "mainland", "greater", or "intl". Defaults to "mainland". |
| industry | string | Industry vertical |
| per_page | integer | Results per page (default 10, max 200) |
| seniority | string | Seniority level (e.g. "Entry (实习/应届)", "Junior (1-3年)", "Mid (3-5年)", "Senior (5-10年)") |
| company_size | string | Company size tier |
| macro_industry |
Retrieve historical salary trend data for a role/city combination over time. Shows how p50 salaries have changed month over month. Use for salary inflation analysis and time-series compensation modeling.
| Argument | Type | Description |
|---|---|---|
| city* | string | City |
| role* | string | Standardized role |
{
"type": "object",
"required": [
"role",
"city"
],
"properties": {
"city": {
"type": "string",
"description": "City"
},
"role": {
"type": "string",
"description": "Standardized role"
}
}
}Get skills ranked by demand frequency in job postings, with associated median salary. Filterable by role, city, region, and skill category. Use for skills gap analysis and tech stack compensation premiums.
| Argument | Type | Description |
|---|---|---|
| city | string | Filter by city |
| role | string | Filter by standardized role |
| limit | integer | Max results (default 20) |
| region | string | Region scope: "mainland", "greater", or "intl". Defaults to "mainland". |
| category | string | Skill category (Backend, Frontend, Cloud, ML, etc.) |
{
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "Filter by city"
},
"role": {
"type": "string",
"description": "Filter by standardized role"
},
"limit": {
"type": "integer",
"description": "Max results (default 20)"
},
"region": {
"enum": [
"mainland",
"greater",
"intl",
"all"
],
"type": "string",
"description": "Region scope: \"mainland\", \"greater\", or \"intl\". Defaults to \"mainland\"."
},
"category": {
"type": "string",
"description": "Skill category (Backend, Frontend, Cloud, ML, etc.)"
}
}
}Get high-level dataset statistics including total companies, salary datapoints, market summaries, distinct roles and cities, and available calculated months. Accepts an optional "region" argument ("mainland", "greater", "intl").
| Argument | Type | Description |
|---|---|---|
| region | string | Region scope: "mainland", "greater", or "intl". Defaults to "mainland". |
{
"type": "object",
"properties": {
"region": {
"enum": [
"mainland",
"greater",
"intl",
"all"
],
"type": "string",
"description": "Region scope: \"mainland\", \"greater\", or \"intl\". Defaults to \"mainland\"."
}
}
}List all genuine metropolitan and prefecture cities available in the dataset. Accepts an optional "region" argument ("mainland", "greater", "intl"). Use before querying market data to ensure accurate city name matching.
| Argument | Type | Description |
|---|---|---|
| region | string | Region scope: "mainland" (Mainland China), "greater" (Mainland + HK + TW), or "intl" (All APAC). Defaults to "mainland". |
{
"type": "object",
"properties": {
"region": {
"enum": [
"mainland",
"greater",
"intl",
"all"
],
"type": "string",
"description": "Region scope: \"mainland\" (Mainland China), \"greater\" (Mainland + HK + TW), or \"intl\" (All APAC). Defaults to \"mainland\"."
}
}
}List all standardized job roles available in the dataset. Use this before querying market summaries to ensure accurate role taxonomy matching.
No arguments.
{
"type": "object",
"properties": []
}List all taxonomies (industries, seniority levels, education levels, company sizes) available in the dataset. Use this to ensure accurate filtering taxonomy matching.
No arguments.
{
"type": "object",
"properties": []
}Search for companies by name (English or Chinese), industry, size tier, or macro industry. Returns firmographic profiles for matching employers. Use for employer discovery and competitive intelligence.
| Argument | Type | Description |
|---|---|---|
| name | string | Company name (partial match, English or Chinese) |
| limit | integer | Max results (default 10) |
| industry | string | Industry vertical |
| size_tier | string | Company size tier |
| macro_industry | string | Macro industry category |
| headquarters_city | string | City of headquarters |
{
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "Company name (partial match, English or Chinese)"
},
"limit": {
"type": "integer",
"description": "Max results (default 10)"
},
"industry": {
"type": "string",
"description": "Industry vertical"
},
"size_tier": {
"type": "string",
"description": "Company size tier"
},
"macro_industry": {
"type": "string",
"description": "Macro industry category"
},
"headquarters_city": {
"type": "string",
"description": "City of headquarters"
}
}
}We read every tool's name, description and inputs the way the model will, looking for instructions hidden from you. A clean result means no warning signs, not a guarantee. Scanned Nov 14, 2026.
No readOnlyHint / destructiveHint annotations, so clients can't tell a read from a write without asking.
Fix: Add annotations to each tool, e.g. { readOnlyHint: true } for tools that only read.
| string |
| Macro industry category |
| education_level | string | Education requirement |
{
"type": "object",
"required": [
"role",
"city"
],
"properties": {
"city": {
"type": "string",
"description": "Target city (e.g. \"Beijing\", \"Shanghai\", \"Shenzhen\", \"Taipei\")"
},
"role": {
"type": "string",
"description": "Standardized role (e.g. \"Software Engineer\", \"Java Engineer\", \"Algorithm Engineer\")"
},
"month": {
"type": "string",
"description": "Calculated month in YYYY-MM format. Defaults to latest available."
},
"region": {
"enum": [
"mainland",
"greater",
"intl",
"all"
],
"type": "string",
"description": "Region scope: \"mainland\", \"greater\", or \"intl\". Defaults to \"mainland\"."
},
"industry": {
"type": "string",
"description": "Industry vertical"
},
"per_page": {
"type": "integer",
"description": "Results per page (default 10, max 200)"
},
"seniority": {
"type": "string",
"description": "Seniority level (e.g. \"Entry (实习/应届)\", \"Junior (1-3年)\", \"Mid (3-5年)\", \"Senior (5-10年)\")"
},
"company_size": {
"type": "string",
"description": "Company size tier"
},
"macro_industry": {
"type": "string",
"description": "Macro industry category"
},
"education_level": {
"type": "string",
"description": "Education requirement"
}
}
}