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TeamedUp Intelligence — China & APAC Salary Data

by io.github.bp46
OverviewDiscussionsAdmin

get_skill_demand

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.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_skill_demand"
  }
}

Input Schema

NameRequiredDescriptionDefault
cityNoFilter by city
roleNoFilter by standardized role
limitNoMax results (default 20)
regionNoRegion scope: "mainland", "greater", or "intl". Defaults to "mainland".
categoryNoSkill category (Backend, Frontend, Cloud, ML, etc.)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations supplied, the description carries the full behavioral burden. It discloses that results are ranked by demand and include median salary and that filtering is supported, but it does not state read-only status, data freshness, pagination behavior, or default limits beyond what the schema implies.

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?

Three short sentences front-load the return content, filters, and intended use cases with no filler. Every sentence contributes to selecting or calling the tool.

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 read-only analytical tool with fully documented optional filters, the description explains what is returned and the main use cases. It leaves out data freshness and explicit output structure details, but with no output schema and a simple parameter surface this is still largely complete.

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 description mentions filtering by role, city, region, and skill category, but the schema already documents all five parameters at 100% coverage, including the limit default. The description adds little parameter meaning beyond confirming those fields act as filters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: getting skills ranked by demand frequency in job postings, paired with median salary. It is clearly distinct from salary-trend, market-summary, and listing tools, though it does not explicitly name or route against any sibling alternative.

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 gives clear intended use cases: skills gap analysis and tech stack compensation premiums. It does not provide when-not-to-use guidance or explicitly compare this tool to the salary or trend siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All TeamedUp Intelligence — China & APAC Salary Data toolsInspect
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