swiss-living-index

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

The description mentions that explicit consent is required and that contact details will be shared with Mirabello Consultancy, which is important behavioral context. However, it does not disclose what happens after submission (e.g., confirmation, response time) or any other side effects. With no annotations, the description carries full burden.

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 (two sentences) and front-loaded with the core purpose. However, it could be slightly more structured (e.g., listing prerequisites) to improve scanability.

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 6 parameters and no output schema or annotations, the description provides the tool's purpose and consent condition but leaves out details about what happens after booking (e.g., confirmation behavior, expected response). It is adequate for a simple booking but could be more 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 coverage is low (17%) with only 'consent' having a description. The description adds little semantic detail for parameters like 'phone' or 'message'; it only mentions 'topic' implicitly by listing example topics. The description does clarify the consent requirement but does not compensate for the lack of parameter 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 action (request a personal consultation), the provider (Mirabello Consultancy), and the specific domain (Swiss relocation & investment-migration). It distinguishes itself from sibling tools by noting it handles queries 'beyond the data'.

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 indicates this tool is for questions beyond what the data tools provide, listing example topics. While it doesn't say when not to use it or name alternatives directly, the context implies it's a human consultation tool, distinct from the data-focused siblings.

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

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

With no annotations provided, the description partially compensates by stating the tool is 'Live' (real-time) and uses official data (ESTV). However, it does not disclose potential error handling, response format, or whether the tool has any side-effects. More details on behavior would be beneficial.

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. The first sentence states the purpose concisely, and the second enumerates parameters with their formats. There is no unnecessary text, making it efficient and front-loaded.

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?

The description covers the input parameters well but does not describe the output (e.g., returns a tax amount in CHF). Given the absence of an output schema, this omission reduces completeness. It also lacks info on error states or required data validation. Overall, it is adequate for simple use but could be more comprehensive.

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 that 'commune' can be name or postcode, specifies units for income and wealth (CHF), and clarifies that 'married' is a boolean and 'children' a number. This adds significant value beyond the raw schema, though it could elaborate on optionality and expected ranges.

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 calculates a live income and wealth tax for a specific commune and household, specifying the source (official ESTV) and year (2026). This distinguishes it from sibling tools like compare_cantons or get_canton, which focus on broader comparisons or canton-level data.

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 when needing a tax estimate for a specific commune and household, but it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites. The guidance is implied but not explicit.

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

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

No annotations provided, and the description fails to disclose behavioral traits such as authentication needs, rate limits, or output format. Only basic functionality is stated.

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?

Two sentences, no fluff, front-loaded with purpose. 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?

Despite moderate complexity (no output schema, no annotations), the description lacks details on what 'all metrics' means, output format, or error handling for invalid canton counts.

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 0%, but the description adds 'cantons = array of abbreviations' and '2-4 cantons', providing some meaning beyond the schema. However, it doesn't clarify what abbreviations are expected.

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 'compare' and the resource '2–4 cantons across all metrics', distinguishing it from sibling tools like 'get_canton' (single canton) and 'rank_by_metric' (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?

No explicit guidance on when to use this tool vs alternatives. The description only mentions the parameter format, not when to prefer it over similar tools.

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

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

No annotations are provided, so the description must carry the full burden. It accurately describes the tool as an estimation calculation with no mention of destructive actions, which is reasonable. However, it lacks details on data freshness, accuracy limitations, or side effects, leaving some behavioral uncertainty.

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?

Three concise sentences: first defines purpose and components, second clarifies savings sign, third highlights importance. No redundant or irrelevant 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 6 parameters and no output schema, the description covers the main aspects: inputs (communes, income, wealth, family), calculations (tax, rent, health), and output interpretation. However, it could mention that wealth and children are optional, and specify the return format beyond a monetary value.

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 67% (4 of 6 parameters have descriptions). The tool description adds overall context (household-level, components) but does not elaborate on individual parameters beyond what the schema already provides. It does not compensate for the missing descriptions for 'married' and 'children'.

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?

Description clearly states the tool estimates net yearly CHF impact of moving between two Swiss communes, specifying components (tax, rent, health premiums). The phrase 'flagship relocation calculation' distinguishes it from siblings like commune_tax (single commune) and compare_cantons (canton-level).

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 provides clear context for when to use the tool (relocation between communes, financial impact evaluation). However, it does not explicitly mention when not to use it or offer alternatives, though the context implies it's the primary relocation tool.

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

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

With no annotations, the description carries the behavioral disclosure burden. It usefully explains rank direction and the caveat that ranks are scenario-relative, but it does not describe return format, error behavior for invalid abbreviations, or explicitly confirm a read-only operation beyond the verb 'get'.

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?

Three short sentences, each contributing necessary information: the operation, the parameter format, and the rank interpretation. There is no redundant wording or padding.

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 simple single-canton retrieval tool, the description covers purpose, input format, and metric interpretation. It does not describe the output structure, but the absence of an output schema is partially mitigated by the phrase 'all metric values' and the tool's straightforward nature.

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 schema only defines 'canton' as a string, but the description adds the crucial format rule: '2-letter abbreviation (e.g. ZH, GE, TI)'. This substantially compensates for the 0% schema coverage, though it does not exhaustively list allowed values.

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 states a specific verb ('Get'), a resource ('metric values'), and an explicit scope ('for one canton'). This clearly distinguishes it from sibling tools like compare_cantons or rank_by_metric, which operate across cantons or single metrics.

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 opening sentence establishes the clear use case: retrieving all metric values for a single canton. It does not explicitly name alternatives or exclusion criteria, but the 'one canton' scope and the provided abbreviation format give practical usage guidance.

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

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

With no annotations, the description carries the behavioral disclosure burden. It reveals that the output is a historical series, that only certain metrics have history, and that SECO unemployment averages have a start boundary at 1993. It does not describe what happens when from_year is omitted, the response shape, or error/empty-data behavior.

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 purpose is front-loaded in the first clause, followed by a compact, high-value list of valid metrics and the SECO caveat. Every clause earns its place and there is no redundant restatement of the schema.

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 simple read-only trend tool with three parameters and no output schema, the description covers the main invocation points: single canton, valid metrics, and optional from_year. The main missing context is the output structure and default start year, which is a modest gap given no output schema or annotations.

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 compensate. It enumerates each valid metric value and explains that metric keys come from list_metrics, and it notes optional from_year. It still does not define the canton format or constraints on from_year, but it materially improves parameter understanding beyond the bare 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?

Description opens with a specific verb and resource: 'Historical year-series for a metric in one canton,' immediately distinguishing get_trend from sibling tools like compare_cantons or rank_by_metric. Examples of supported series (sunshine to 1884, migration to 1981) anchor the intended use.

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?

It tells the agent to use a metric key from list_metrics, restricts to metrics that have history, and lists the accepted metric names, plus optional from_year. It does not explicitly state when not to use it or name alternative tools, but the 'one canton' scope and metric filtering give clear context.

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

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

No annotations exist, so the description carries the full burden. It clearly states the output includes key, label, unit, source, and interpretation of higher/lower values, which effectively communicates the tool's behavior beyond a simple list.

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?

A single sentence that efficiently conveys the tool's purpose and output content with no extraneous words. Every part is meaningful.

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 (no parameters, no output schema), the description fully explains the return fields and covers the intended functionality. No gaps remain.

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, and the schema coverage is 100% (empty). The description adds value by detailing what the output contains, justifying a baseline of 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 a specific verb ('List') and resource ('available metrics') and details the information provided (key, label, unit, source, meaning). It clearly distinguishes from sibling tools like 'book_consultation' or 'get_canton'.

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. As a simple listing with no parameters, usage is implied as a preliminary step, but no exclusions or alternatives are mentioned.

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

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

With no annotations provided, the description must disclose all behavioral traits. It explains that the tool ranks all cantons, takes a metric from list_metrics, and an optional order. However, it does not describe the return format, whether it is read-only, or any side effects. The transparency 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 exceptionally concise: two sentences with no wasted words. Every sentence adds value, stating the purpose and clarifying parameter usage without 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?

Given the tool's simplicity (two parameters, no output schema), the description covers the essential aspects: what it does, how parameters are used, and the source of valid metric values. It could be slightly improved by noting the output format, but overall it is sufficiently 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 0%, so the description must add meaning beyond types. It explains that 'metric' is a key from list_metrics and that 'order' can be 'high' or 'low' with default 'high'. This provides essential semantics that the schema alone lacks.

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 action ('Rank all cantons by a metric') and specifies the resource ('cantons'). It also explains the source for the metric parameter and the valid values for order, making the purpose unambiguous.

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 the tool (to rank cantons based on a metric) but does not explicitly state when not to use it or mention alternatives (e.g., compare_cantons or get_canton). The sibling context provides some implicit differentiation, but the description itself lacks explicit guidance.

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

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

With no annotations provided, the description carries the full behavioral burden, and it does substantial work: it discloses the percentile method, that all 26 cantons are scored, that each evidence figure carries value/unit/rank/source/year, and that scores reflect order under the stated weights, not universal suitability. It does not discuss response shape or side effects, but for a recommendation tool these are reasonably covered.

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 dense but every sentence earns its place: it front-loads the tool's purpose, then gives the category definitions, the scoring method, and the crucial caveat that results are relative under stated weights. There is no fluff or repetition of schema fields.

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 an 8-parameter tool with no annotations and no output schema, the description provides enough context to understand the weighted-scoring semantics, the parameter meaning, and the kind of evidence returned. It does not explain default behavior when weights are omitted or exactly how infra fits alongside the five named categories, but the schema partially covers those aspects.

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 only 50%, so the description compensates by decoding five category parameters into concrete submetrics such as cost = rent/premiums/electricity and env = sunshine/crime/vacancy, while confirming the 0–3 weight range. It does not add meaning for infra or international_community beyond the schema, but those parameters already have 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 names a specific verb and resource: 'Personal canton recommendation' for deciding where to live in Switzerland, and it clearly distinguishes the tool from generic ranking by describing weighted 0–3 category inputs and the transparent percentile method. This also differentiates it from siblings like compare_cantons and rank_by_metric, which would not be personal or weighted.

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 use case is implied through 'Where should I live in Switzerland?' and the weighting model, so an agent can infer when personal preferences matter. However, it never explicitly states when to prefer this over compare_cantons or rank_by_metric, and gives no exclusion criteria or prerequisites.

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

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

The description mentions the official register and the returned fields, giving insight into the tool's behaviour. However, it does not disclose details like case sensitivity, partial matching, or whether both name and postcode can be combined, but for a simple lookup it is adequate given no annotations.

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?

Two efficient sentences with no redundancy; the purpose and usage guidance are front-loaded, and 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?

For a simple tool with one parameter, no output schema, and no nested objects, the description is complete: it defines input, output, and usage context relative to siblings.

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 input schema has one parameter 'query' with no description, but the tool description adds meaning by stating that the query can be a name or postcode, thereby explaining its usage beyond the bare 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 action ('Find Swiss communes'), the inputs (name or postcode), the data source (official ESTV location register), and the returned fields (name, canton, postcode). It also distinguishes itself from sibling tools by specifying when to use it.

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?

Explicitly says to use this tool before 'commune_tax' or 'estimate_net_move' when spelling is uncertain, providing clear guidance on when it is appropriate and what alternatives are related.

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

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

With no annotations, the description transparently discloses the data source (official Federal Chancellery/VoteInfo), languages (DE/FR/IT/EN), and that subjects are provided 'once announced'. It does not detail update frequency or caching, but for a read-only info tool, this is sufficient.

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, well-structured sentence that front-loads the key purpose and covers all essential details without 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 zero-parameter tool with no output schema, the description adequately covers purpose, content, source, and languages. It does not specify behavior when no votes are upcoming, but this is minor given the clear domain.

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?

No parameters exist, so the description correctly explains what the tool returns (dates and ballot subjects). The schema coverage is 100% implicitly, and the description adds context about data source and languages beyond the empty 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 provides 'next Swiss federal vote dates' and 'subjects on the ballot' in multiple languages, distinguishing it from sibling tools like 'commune_tax' or 'get_canton' which focus on other domains.

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 retrieving upcoming federal vote information, but does not explicitly mention when not to use or compare with alternatives. However, the context is clear and no exclusion is necessary given the tool's unique purpose.

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