_ registry / mcp streamable-http · checked 37m ago

TeamedUp Intelligence

https://intelligence.teamedupchina.com

Registry code: 5266af6b336b1e75

api record

China & APAC salary benchmarks (319k+ jobs), statutory cost (五险一金), and employer intelligence.

from a public catalogue that lists it, not from the operator

endpoint
https://intelligence.teamedupchina.com/api/v1/mcp
protocol
streamable-http ·2024-11-05
authentication
none observed
public key
none — nobody has proven they own this listing · is it yours? claim it
karma
0 · newcomer
_ is it live, free and safe measured by this hub
Is TeamedUp Intelligence live?
Yes — it answered the hub's last check (checked 37m ago). It answered 100% of checks over the last 30 days.
Is TeamedUp Intelligence free to use?
Not anonymously — it asks for a key or a login before it will serve. Whether that key costs anything is the operator's plan to say: the hub measured the door, not the pricing.
What tools does TeamedUp Intelligence have?
13 tools: get_salary_trend, list_roles, list_taxonomies, get_company_info, get_skill_demand, get_stats, get_market_summary, get_dataset_trends, ….
Is TeamedUp Intelligence safe to connect?
The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.
reachable
live
uptime, 30 days
100%

90 days 100%· all time 100%

latency
347ms

last good check

priced tools
0

of 13 tools

_ answered our checks, 90 days 1 checks · signed record
  • unknown → live
_ usage and payments 30 days

Calls placed through this hub's router, from its own receipts. Every caller and every payer counts the same; the chain total is counted from three payers.

accounts
0

through this hub

calls served
0

successful

paid through this hub
0 USDC

what callers paid

_ what it can do 13 tools
13 auth-required 13 of 13 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • get_salary_trend auth-required never probed

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "role",
    "city"
  ],
  "properties": {
    "city": {
      "type": "string",
      "description": "City"
    },
    "role": {
      "type": "string",
      "description": "Standardized role"
    }
  }
}
    arguments 17 lines
  • list_roles auth-required never probed

    List all standardized job roles available in the dataset. Use this before querying market summaries to ensure accurate role taxonomy matching.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • list_taxonomies auth-required never probed

    List all taxonomies (industries, seniority levels, education levels, company sizes) available in the dataset. Use this to ensure accurate filtering taxonomy matching.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • get_company_info auth-required 37m ago

    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.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "name": {
      "type": "string",
      "description": "Company name (English or Chinese), supports partial match"
    },
    "company_id": {
      "type": "integer",
      "description": "Exact company ID"
    }
  }
}
    arguments 13 lines
  • get_skill_demand auth-required 37m ago

    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.

    mcp-tool

    {
  "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.)"
    }
  }
}
    arguments 31 lines
  • get_stats auth-required 37m ago

    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").

    mcp-tool

    {
  "type": "object",
  "properties": {
    "region": {
      "enum": [
        "mainland",
        "greater",
        "intl",
        "all"
      ],
      "type": "string",
      "description": "Region scope: \"mainland\", \"greater\", or \"intl\". Defaults to \"mainland\"."
    }
  }
}
    arguments 15 lines
  • get_market_summary auth-required never probed

    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.

    mcp-tool

    {
  "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"
    }
  }
}
    arguments 55 lines
  • get_dataset_trends auth-required never probed

    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.

    mcp-tool

    {
  "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"
    }
  }
}
    arguments 28 lines
  • search_companies auth-required never probed

    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.

    mcp-tool

    {
  "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"
    }
  }
}
    arguments 29 lines
  • list_cities auth-required never probed

    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.

    mcp-tool

    {
  "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\"."
    }
  }
}
    arguments 15 lines
  • get_company_jobs auth-required never probed

    Retrieve paginated historical job postings and salary bands for a specific company by its ID.

    mcp-tool

    {
  "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"
    }
  }
}
    arguments 20 lines
  • calculate_all_in_employer_cost auth-required never probed

    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.

    mcp-tool

    {
  "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."
    }
  }
}
    arguments 36 lines
  • calculate_compensation_arbitrage auth-required never probed

    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.

    mcp-tool

    {
  "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\")"
    }
  }
}
    arguments 34 lines
_ try it through the hub, ceiling 0

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_ how we know
card completeness
100%

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spec deviations
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median latency
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work
attempts
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rejected
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acceptance rate
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settled without a human
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upheld
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reviews
paid reviews
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