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GUTHMANN® Berlin Real Estate

by estate.guthmann

Berlin Apartment Buildings

market_berlin_apartment_buildings
Read-onlyIdempotent

Berlin apartment building market (rental buildings, "Zinshäuser") - transaction data. Source: transaction data from the Berlin committee of valuation experts (GAA, Gutachterausschuss).

Parameters:

  • rbs: "stadt" | "bezirk" | "stadtteil" | "ortsteil" — bezirk = Borough, ortsteil = District

Time filters:

  • start/end: flexible date values (YYYY, YYYY-MM, YYYY-MM-DD)

  • recent: number of most recent periods (alternative to start/end)

Additional filters:

  • bezirk_id, stadtteil_id, ortsteil_id

  • limit, fields, include: "metadata"

Key response fields:

  • transaktionspreis: average total purchase price (€)

  • transaktionen: number of transactions

  • vielfaches: purchase price multiplier (purchase price / annual net cold rent)

  • vielfaches_delta_*j: absolute change of the multiplier (1y, 5y, 10y, 15y)

  • transaktionspreis_m2: price per m² of living space

Ideal for: investment market analysis, yield comparisons across locations

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNo
rbsNostadt
limitNoMaximale Anzahl Ergebnisse (öffentlich bis 1000)
startNo
fieldsNo
recentNo
includeNo
bezirk_idNo
ortsteil_idNo

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare read-only, open-world, idempotent, non-destructive behavior. The description adds useful context beyond that by naming the data source and clarifying key response fields such as transaktionspreis and vielfaches_delta. No contradiction with the annotations exists.

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?

The description is well-structured with clear sections for parameters, time filters, additional filters, and key response fields, and the main purpose is front-loaded. It avoids unnecessary prose while packing in the details an agent needs.

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 tool with no output schema and low schema coverage, the description compensates partially by listing key response fields and date formats. Yet it never defines the period granularity behind 'recent', does not explain 'fields' syntax, and contains the stadtteil/stadtteil_id inconsistency, so it is not fully 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?

With only 11% schema coverage the description carries most parameter documentation and does explain rbs, start/end, recent, and include. However, it lists rbs value 'stadtteil' and a 'stadtteil_id' filter that are not present in the input schema, and it leaves 'fields' semantics vague, creating a real invocation hazard.

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?

The description clearly identifies the resource as Berlin apartment building transaction data ('Zinshäuser') from the GAA, which sets it apart from siblings like market_berlin_apartments and market_germany_apartment_buildings. The 'Ideal for' line reinforces the intended use for investment market analysis and yield comparison.

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?

The 'Ideal for' clause gives concrete use cases (investment analysis, yield comparisons), helping an agent decide when to call this tool. It does not explicitly name sibling alternatives or state when not to use it, so it misses the full exclusion guidance needed for a 5.

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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TDQS

B3.4/5.0
Disambiguation5/5

Each tool targets a distinct resource/action: lists, details, search, and market/macro/socio datasets are clearly separated by prefixes and German domain terms. Even potentially similar tools like immo_berlin_wohnungen and immo_de_wohnimmobilien are disambiguated by Berlin vs national scope.

Naming Consistency5/5

Tool names follow a consistent snake_case pattern: plural nouns for list tools, singular_details for detail tools, and domain prefixes like berlin_, immo_, makro_, and sozio_. German naming is uniformly applied, making the set predictable once the pattern is recognized.

Tool Count2/5

With 34 tools, the set is larger than an agent can comfortably scan and evaluate in a single decision context. Although the tools are organized into clear domains and each serves a real purpose, the volume is beyond the 'well-scoped' threshold and starts to impose meaningful selection overhead.

Completeness3/5

The set covers geography, real estate markets, macroeconomics, and socio-demographic data extensively, but there is a notable gap: rbs='stadtteil' is used across multiple tools yet no dedicated stadtteil-list or stadtteil-details endpoint exists. Agents cannot easily resolve stadtteil_id values, which creates a dead end for that aggregation level.

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