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arbeitsagentur-mcp

arbeitsagentur-mcp

by slopmaxxer
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_salaries

Read-onlyIdempotent

Retrieve gross monthly salary benchmarks (median and quartiles) by occupation code, region, industry, age, gender, and requirement level to assess job offers and set salary expectations.

Instructions

Gross monthly salaries from the BA Entgeltatlas (median and quartiles of full-time employees).

Useful for judging whether a job offer is reasonable (Zumutbarkeit) and for salary expectations.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "kldb": "<kldb>"
    },
    "name": "get_salaries"
  }
}

Input Schema

NameRequiredDescriptionDefault
ageNo
kldbYesKldB 2010 code (3-5 digits), e.g. '43412' or 'B 43412'; get it from get_profession
levelNoRequirement level (only for 3-4 digit codes)
genderNo
regionNo'Deutschland' (default), 'Ostdeutschland', 'Westdeutschland', a Bundesland or a big city like 'Leipzig'
industryNoIndustry filter, e.g. 'information_kommunikation', 'oeffentlicher_dienst_bildung_gesundheit_soziales'

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare read-only, open-world, and idempotent behavior. The description adds important domain context—gross monthly, median/quartiles, full-time employees—that clarifies what the numbers represent, though it omits return format and rate limits. With annotations covering the safety profile, a 4.

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?

Two sentences, front-loaded with the core data and followed by use cases. No wasted words, appropriate length. Score 5.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 6-parameter tool with an output schema and rich annotations, the description covers the what and why but omits how parameters filter results or that kldb is required (schema covers required). It is adequate but leaves gaps against the complexity. Score 3.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds no parameter-level meaning. The schema documents 4 of 6 parameters (67% coverage), but age and gender lack descriptions, and the description does not compensate. Score 2.

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 names the specific data source (BA Entgeltatlas) and the exact statistics returned (gross monthly median and quartiles for full-time employees), making the tool's purpose unambiguous. It does not explicitly differentiate from sibling tools, but the resource is unique enough that an agent can tell it apart from get_profession or search_jobs.

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?

Provides clear context for use (judging job offer reasonableness, salary expectations) but does not state when to avoid it or name alternative tools. This is clear context without exclusions, fitting a 4.

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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