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agentic-firmenbuch

agentic-firmenbuch

by jkbngb
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Dataset coverage

get_coverage
Read-onlyIdempotent

Assess how complete Austria's Firmenbuch dataset is by counting companies with parsed XML financials, PDF-only filings, or no data.

Instructions

How much of the register we actually serve, as an aggregate dashboard. Read-only, no parameters.

    Returns coverage counts broken down by data availability — companies with parsed XML
    financials vs PDF-only (linked but not machine-readable) vs none — and by format/status, so
    you can gauge what share of the universe has usable financials. It is a dataset-wide
    overview (served O(1) from a precomputed stats doc), NOT per-company data, no filters. Use
    for "how complete is the data"; for the valid filter values use list_sectors, for one
    group's aggregate figures use get_cohort_summary.

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_coverage"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive/no-open-world, so safety is covered. The description adds real context beyond that: it is dataset-wide, not per-company, accepts no filters, and is served O(1) from a precomputed stats doc, plus it sketches the return breakdown (XML vs PDF-only vs none).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads the core purpose and then the return breakdown and routing, with each sentence carrying weight. Slightly verbose in places ('as an aggregate dashboard'), but no wasted filler.

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

Completeness5/5

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

Complete for a parameterless, read-only aggregate tool. An output schema exists so return values are covered, and the description still adds scope, performance, and routing context that fully equips an agent to call it correctly.

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

Parameters4/5

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

Zero parameters, which is the baseline-4 case. The description reinforces this with 'no parameters' and 'no filters', leaving no ambiguity that no arguments are accepted.

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

Purpose5/5

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

States a specific verb+resource: an aggregate, dataset-wide coverage dashboard showing how much of the register is served. It explicitly distinguishes itself from siblings (list_sectors, get_cohort_summary) and from per-company data, so an agent can identify it immediately.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit use case ('how complete is the data') and names two alternatives with the conditions that select them: list_sectors for valid filter values, get_cohort_summary for one group's aggregate figures. Nothing is left to inference.

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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