german-tax-data
Server Details
German municipal tax data (Hundesteuer, Zweitwohnungsteuer, Pfaendung), cited to the source
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.1/5 across 9 of 9 tools scored.
Each tool targets a distinct resource or operation: dog tax (state, lookup, ranking), second-home tax (state, lookup, ranking, changes), dataset listing, and wage garnishment calculation. No overlap in purpose.
Tool names follow a consistent pattern using snake_case with domain prefixes and action suffixes (e.g., 'hundesteuer_lookup', 'zweitwohnungsteuer_by_state'). The single 'list_datasets' and 'pfaendung_calc' also follow the same verb/noun structure.
9 tools is appropriate for the domain—covering multiple facets (lookup, ranking, state aggregates, changes) for two tax types plus a general dataset overview and a calculator.
The tool set provides comprehensive coverage for the available datasets (dog tax, second-home tax, wage garnishment) with state aggregates, rankings, lookups, and change tracking, plus a dataset listing to orient users.
Available Tools
9 toolshundesteuer_by_stateAInspect
Average German dog tax (Hundesteuer, first dog per year) per Bundesland (federal state), aggregated over the covered largest cities — the data behind the Deutschlandkarte. Answers 'which German state has the highest/lowest dog tax'. Note: a mean over cities, not an official state rate (the tax is municipal).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses an important caveat: the data is a mean over cities, not an official state rate. This is beyond what annotations provide (none given). It does not cover all behavioral aspects but is sufficient for the tool's simplicity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences that efficiently state the purpose and a caveat. No fluff, front-loaded with the key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and no output schema, the description fully explains its purpose, the data it returns, and its limitations. It is complete for an agent to decide when to invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description does not need to add parameter info. It adds value by explaining what the tool does without relying on parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns average dog tax per federal state, aggregated from cities. It specifies the resource (state-level averages), verb (answers which state highest/lowest), and distinguishes from siblings like hundesteuer_lookup (city-level) and hundesteuer_ranking.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
While the description implies the tool is for state-level comparisons, it does not explicitly mention when to use alternatives like hundesteuer_lookup for city data or hundesteuer_ranking for ordering. No when-not-to or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
hundesteuer_lookupAInspect
Look up the German dog tax (Hundesteuer) for a city: annual rate for one or more dogs, listed-dog (Listenhund) surcharge, the city's pricing model, its rank among the largest cities, plus the official source and Stand. Rates verified against each municipal Satzung.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | City name or slug, e.g. "Köln", "muenchen", "Frankfurt" | |
| dogs | No | Number of dogs (default 1) | |
| listed | No | Treat as a listed/dangerous-breed dog (Listenhund) if the city has a higher rate |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that rates are 'verified against each municipal Satzung', indicating data reliability. It does not mention destructive actions, rate limits, or error conditions, but as a read-only lookup, the description provides adequate behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, moderately long sentence that front-loads the main purpose. It packs many details without being overly verbose. A slight improvement could be breaking into two sentences for readability, but it remains concise and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description sufficiently covers return values. It also notes that rates are verified against municipal Satzungen. However, it does not clarify the geographic scope (e.g., 'largest cities' set) or potential limitations like city coverage. Overall adequate for a look-up tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with all parameters described. The description adds value beyond the schema by specifying the exact output fields (annual rate, surcharge, model, rank, source, Stand) and the verification method. This helps the agent understand what the returned data will contain.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb phrase 'Look up the German dog tax (Hundesteuer) for a city' and enumerates exact data fields returned (annual rate, listed-dog surcharge, pricing model, rank, source, Stand). It clearly distinguishes from sibling tools like hundesteuer_by_state and hundesteuer_ranking by being city-specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool (for city-level tax lookup) and the sibling tools listed in context signal provide alternatives (by state, ranking). However, it does not explicitly state when not to use this tool or provide direct comparison with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
hundesteuer_rankingAInspect
Ranking of German cities by Hundesteuer (first dog, per year): most expensive or cheapest, plus the national average across the covered cities.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many cities to return | |
| order | No | expensive |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It mentions the ranking and average but does not disclose data freshness, pagination beyond the limit parameter, or any needed authorizations. It is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that immediately states purpose and key options. No fluff, front-loaded, every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With two parameters and no output schema, the description conveys the essential functionality: ranking and average. It could be more specific about output format or data coverage, but for a simple ranking tool, it is fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50%: the limit parameter has a description, but order does not. The overall description adds context (first dog, per year, national average) that helps interpret the parameters, but does not describe individual parameter semantics beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it ranks German cities by Hundesteuer (first dog, per year) with options for expensive or cheapest, and includes the national average. It distinguishes from sibling tools like hundesteuer_by_state and hundesteuer_lookup by focusing on a general ranking.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for getting a ranking of cities, but lacks explicit guidance on when to use this versus other tools, or any exclusions. Sibling tool names provide context but are not mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_datasetsAInspect
List the available hand-verified German municipal tax/fee datasets (Hundesteuer, Zweitwohnungsteuer, Pfändung), their coverage, source sites and refresh cadence. Call this first to see what you can query.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the tool returns a list with coverage, source sites, and refresh cadence. It implies a read-only operation without side effects, which is appropriate for a listing tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the action, and every word adds value. No unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately explains what is returned (list of datasets, coverage, source sites, refresh cadence). It is complete for a discovery tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema coverage is 100% trivially. The description does not need to add parameter info. Baseline for 0 parameters is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'list' and identifies the resource as 'available hand-verified German municipal tax/fee datasets' with concrete examples (Hundesteuer, Zweitwohnungsteuer, Pfändung). It clearly distinguishes from sibling tools which are specific query tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states 'Call this first to see what you can query,' providing clear guidance on when to use this tool (before other tools). It does not explicitly mention when not to use or provide alternatives, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pfaendung_calcAInspect
Compute the attachable portion of monthly net income under German §850c ZPO (Pfändung / wage garnishment), using the current official Pfändungsfreigrenzenbekanntmachung. Returns attachable amount, remaining amount, exempt base and the P-Konto base. Informational, not legal advice.
| Name | Required | Description | Default |
|---|---|---|---|
| dependents | No | Number of maintenance-dependent persons (0-5) | |
| net_income_eur | Yes | Monthly NET income in EUR |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the tool uses the current official Pfändungsfreigrenzenbekanntmachung and returns specific values. It also notes the tool is informational, not legal advice. This provides adequate transparency for a computation tool with no side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, using two sentences to convey the purpose, legal basis, outputs, and limitations. It is front-loaded with the core action and contains no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 parameters, no output schema), the description explains the return values adequately. However, it could be more complete by mentioning that dependents parameter affects the calculation or noting that the tool uses specific thresholds. Still, for a straightforward computation, it provides sufficient context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with both parameters having descriptions in the input schema. The description does not add any additional meaning beyond what the schema provides (e.g., specifying that dependents are maintenance-dependent persons and net income is monthly). Thus, the description meets the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool's purpose: computing the attachable portion of net income under German §850c ZPO. It specifies the legal framework, the input (monthly net income), and the outputs (attachable amount, remaining amount, exempt base, P-Konto base). This is distinct from sibling tools which deal with Hundesteuer and Zweitwohnungsteuer.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states that the tool is informational and not legal advice, but it does not explicitly state when to use this tool versus alternatives. While no sibling tools perform similar functions, the lack of explicit usage guidance or clear when-not-to-use conditions results in a score of 3.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
zweitwohnungsteuer_by_stateAInspect
Average German second-home tax (Zweitwohnungsteuer) rate in percent per Bundesland (federal state), aggregated over the levying cities with a computable rate — the data behind the Deutschlandkarte. Answers 'which German state has the highest/lowest second-home tax'. Note: a mean over cities, not an official state rate (the tax is municipal).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It explains the data is a mean over cities and not official state rates, which is key. However, it does not disclose data freshness or whether the tool is compute-on-demand or cached.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph, front-loading the main purpose. It includes necessary caveats without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no parameters and a simple output, the description is complete. It states the output format (percent), aggregation level (state), and notes about municipal tax. It also mentions the specific questions it answers.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has no parameters (0 params), so baseline is 4. The description adds value by explaining the output (state-level averages) without needing to document parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns average second-home tax rate per state, specifies it's in percent, aggregated over cities, and distinguishes it from official state rates. It also mentions it answers questions about highest/lowest, which adds clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for state-level averages but does not explicitly guide when to use this vs. sibling tools (e.g., lookup for specific city, ranking for city ranking). No when-not-to-use or alternative references are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
zweitwohnungsteuer_changesAInspect
List German cities that changed (raised or newly introduced) their Zweitwohnungsteuer in a given year — the fresh-news angle. Defaults to 2026 (Stuttgart, Chemnitz, Heidelberg doubled to 20%, etc.).
| Name | Required | Description | Default |
|---|---|---|---|
| year | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool lists changes and defaults to 2026, but lacks disclosure of potential rate limits, authentication needs, pagination, or behavior when no changes exist. It is adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with a parenthetical example. It is front-loaded with the main action and key information, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter, no output schema, and a simple list function, the description is mostly complete. It covers the purpose, parameter, and default. It could mention output format but is not critical for selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add meaning. It explains the 'year' parameter by giving a default (2026) and examples, but does not specify the format or constraints (min/max) beyond the schema. It provides some value but not fully compensating.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List', the resource 'German cities that changed their Zweitwohnungsteuer', and the specific angle 'in a given year — the fresh-news angle'. It distinguishes from sibling tools like lookup (specific city) or ranking (ranking) by focusing on changes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for checking recent changes (fresh-news angle) and provides a default year (2026) with examples, but does not explicitly state when to use this tool versus alternatives like lookup or ranking, nor does it mention when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
zweitwohnungsteuer_lookupAInspect
Look up the German second-home tax (Zweitwohnungsteuer) for a city: whether the city levies it, the rate and its basis (net cold rent etc.), the rank among cities, official source and Stand. Optionally pass a monthly cold rent to get the annual tax. Returns state = keine_steuer | berechenbar | sondermodell.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | City name or slug, e.g. "Stuttgart", "muenchen", "Konstanz" | |
| monthly_rent | No | Monthly cold rent (Kaltmiete) in EUR, to compute the annual tax (only for computable cities) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: returns a state ('keine_steuer', 'berechenbar', 'sondermodell'), and optionally computes annual tax when monthly rent is provided for computable cities. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured, using clear language and listing key outputs. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a lookup tool with two parameters and no output schema, the description is complete: it explains inputs, behavior with optional rent, and the three possible return states. No missing information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters. The tool description adds value by explaining the purpose of monthly rent (computing annual tax) and the return state, going beyond the schema's basic descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: looking up German second-home tax for a city, specifying the returned information (whether levied, rate, basis, rank, source, Stand) and the optional monthly rent parameter for annual tax computation. It distinguishes from sibling tools like 'zweitwohnungsteuer_by_state' and 'zweitwohnungsteuer_ranking' by focusing on a single city lookup.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for single-city queries, and sibling tool names (e.g., 'zweitwohnungsteuer_by_state', 'zweitwohnungsteuer_ranking') provide context for when to use alternatives. However, it does not explicitly state when not to use this tool or mention specific alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
zweitwohnungsteuer_rankingAInspect
Ranking of German cities by Zweitwohnungsteuer rate: highest or lowest, plus the average, count with a computable rate, and count of cities with no second-home tax.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many cities to return | |
| order | No | expensive |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries the burden. It discloses that the tool returns a ranking, average, count with computable rate, and count of cities with no tax. However, it does not mention data freshness, whether the ranking is based on actual rates, or any access constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that efficiently conveys the tool's purpose and output. No fluff, front-loaded with key action verb 'Ranking'.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, but description lists return elements (ranking, average, counts). Adequate for a simple ranking tool, though missing details like unit of rate or pagination.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (limit has description, order does not). The tool description clarifies the order parameter with 'highest or lowest' and implies limit as part of ranking, adding value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool ranks German cities by Zweitwohnungsteuer rate, with options for highest or lowest, and mentions aggregate statistics. It distinguishes itself from sibling tools like lookup and other rankings by specifying the tax type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like 'zweitwohnungsteuer_lookup' or 'zweitwohnungsteuer_by_state'. Usage is implied through context but lacks clear when-not-to-use or prerequisites.
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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