getecoback-climate-weather

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

With no annotations, the description carries full responsibility for behavior. It transparently discloses that there is no federal purchase premium (only VAT exemption), that programmes vary by state, and that it provides an amortization calculation. It also notes the Stand (08/2026), showing currency. It doesn't describe potential limitations like lack of exact programme details, but it is notably honest about what the tool does and does not include.

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, dense sentence that front-loads the main topic (Balkonkraftwerk-/Speicher-Förderung in Deutschland) and then efficiently lists key covered points (state programmes, ~100€ bonus, apply-before-buying, payback math, no federal premium). While it is long, every clause adds distinct information 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?

Given the tool has no output schema and 4 optional parameters, the description is solid: it states the topic, key facts, and the central calculation (payback with grant). It doesn't describe exactly what the output format will be, but for an informational tool this is acceptable. The caveat about no federal premium is important and included, making the description contextually adequate.

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 already provides full descriptions for all four parameters (100% coverage). The tool description adds context by linking the parameters to the payback calculation (e.g., preis_eur, zuschuss_eur, ersparnis_eur_jahr) and mentions the ~100€ storage bonus, which helps the agent understand how zuschuss_eur might be used. This goes 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 identifies the tool's purpose: explaining German subsidies for balcony solar/storage systems and how a grant shortens payback. It specifies the resource (Balkonkraftwerk-/Speicher-Förderung), scope (Germany, state programmes), and covers payback arithmetic, which distinguishes it from the sibling tools that focus on other home-energy topics.

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 on what the tool covers (state programmes, storage bonus, apply-before-buying rule) and implies when to use it (when researching balcony solar subsidies). It doesn't explicitly state when not to use it or name alternatives, but the context is sufficient given the unique focus.

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 takes full responsibility. It transparently discloses the calculation formula (340 BTU/m² × sun factor), the sun factor's role, and the geographic scope (Germany/Europe). It also mentions the 'matching device class' output. It does not describe response format or edge cases, but for a simple calculator this is sufficient behavioral disclosure.

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 concise lines (bilingual), front-loaded with the primary purpose. Every clause adds value: formula, region, and device class. No filler or redundant repetition of schema details. Very efficient.

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 calculator with two parameters and no output schema, the description covers all essential context: what the tool computes, the formula, the unit, the geographic applicability, and the output class. It is complete enough for an agent to select and invoke it correctly without missing critical 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 description coverage is 100%, giving baseline 3. The description adds extra value by explaining the 'sun factor' concept linked to the 'sonne' parameter and explicitly mapping 'qm' to the 'X m²' in the formula. This goes beyond the schema's simple field names and descriptions, enriching semantic understanding.

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: 'Empfohlene Kühlleistung (BTU) für einen Raum' (recommended cooling capacity in BTU for a room) and "with the matching device class". It uses specific verbs and resources, and distinguishes it from sibling tools like heizleistung_watt (heating) and taupunkt_lueften (dew point). The question 'how many BTU do I need for X m²?' makes the use case explicit.

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 gives context on when to use the tool: for rooms in Germany/Europe, with the formula and sun factor. It implies it's the cooling counterpart to heating tools. However, it does not explicitly name alternatives or exclusions like 'use heizleistung_watt for heating', so it lacks the explicit when-not guidance for a perfect 5.

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 full behavioral transparency burden. It discloses the calculation formula (perimeter = 2×(width+height)) and the output includes an off-the-shelf size, but it does not state that the operation is a pure read/calculation, nor describe edge-case behavior (e.g., out-of-range inputs). The provided context is helpful but not exhaustive for a calculator with no output schema.

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, bilingual but compact, with the formula in parentheses. It front-loads the primary purpose and adds the formula and scope in the second sentence. No redundant information or filler; every sentence contributes.

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 relatively simple calculator with no output schema, the description is fairly complete: it states the inputs (sash measurements), the formula, the covered window types, and the output (length plus off-the-shelf size). However, it does not explicitly describe the exact return structure or units, which would be helpful given the lack of an output schema. Still, the description covers the essential aspects well.

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 100%, so the baseline is 3. The description adds value by explaining the relationship between parameters (perimeter formula) and clarifying that 'breite_cm' is the movable part of the sash (already in schema but reinforced in the description). It also maps 'fenstertyp' to tilt-and-turn/roof windows, which aligns with the enum.

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: calculating the required window-seal length for portable air conditioners based on sash dimensions, plus the off-the-shelf size. It specifies the formula and the window types covered (tilt-and-turn and roof windows), distinguishing it from the sibling tools that deal with BTU, wattage, heatwave predictions, etc.

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 gives clear context for when to use this tool (for window seal length from sash measurements, for portable ACs) and what it covers (tilt-and-turn/roof windows). It does not explicitly name alternative tools or exclusions, but the specificity of the calculator's scope makes the usage context apparent.

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 carries the behavioral disclosure burden. It does disclose important outcome traits: it returns a device family, honest physics, appropriate room sizing, and a matching guide. However, it does not describe the exact response shape, edge cases, or whether the tool performs any side effects; for a recommendation-style tool, this is a reasonable but not rich level of transparency.

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 compact: one bilingual purpose sentence plus a sibling-relation sentence. It front-loads the core purpose and avoids filler while still conveying the problem scope, output content, and hierarchy among tools.

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 no output schema, the description still conveys the essential result: a device-family recommendation with physics rationale, sizing, and guide. Parameter semantics are fully covered by the schema, and the sibling relationship is stated. A more explicit return format would improve completeness, but for a two-parameter selector this is sufficient.

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%, and the schema already provides meaningful descriptions for both parameters, including the problem enum values and qm range/default. The description only loosely reinforces 'right size for the room' and lists the same problem categories, so it adds little semantic value beyond the schema. Baseline 3 applies.

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 concrete goal: 'Which device family solves a given indoor-climate problem?' and enumerates the problem categories (too hot, damp/mould, too cold, stale air). It also specifies the value-adds (honest physics, correct room sizing, matching guide). It explicitly distinguishes itself from related sizing tools by naming btu_empfehlung/heizleistung_watt as the layers below 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?

The description gives clear context: use this tool when the user has an indoor-climate problem and needs a device family recommendation before technical sizing. It positions the tool as 'the decision layer above' btu_empfehlung/heizleistung_watt, implying a sequence. It lacks explicit when-not-to-use or alternative-conditions, but the context is not ambiguous.

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 full transparency burden. It discloses the calculation model (60/80/100 W/m² for different insulation levels) and the fact that running costs per full-load hour are included. It does not mention error handling or exact output format, but for a calculation tool this is reasonably transparent.

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 concise sentences, bilingual, and front-loaded with the tool's purpose. It avoids redundancy and includes only the most essential information about inputs and outputs.

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 relatively simple calculation tool with no output schema and no annotations, the description adequately explains the purpose, calculation basis, and the additional cost output. It could be more explicit about the return structure, but it is sufficiently complete for an agent to understand the tool's behavior.

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% and the description adds little beyond repeating the schema's own parameter explanations. The description mentions the insulation standards and running costs, but these are already described in the schema for daemmung and strompreis_euro_kwh, so it does not meaningfully enhance parameter understanding.

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 that the tool provides the required heating power in watts for a room, based on floor area and insulation standard. It distinguishes itself from sibling tools by focusing on electric/infrared heating power and the specific calculation factors, though it lacks an explicit verb like 'calculate'.

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 gives useful context (for rooms, from area and insulation), implying when this is relevant. However, it does not explicitly state when to use this tool over alternatives such as btu_empfehlung, nor does it mention exclusions or prerequisites beyond what the schema lists.

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?

Even though no annotations exist, the description discloses the key behavioral aspects: the time span (3 days), locations (3 cities), data source (open-meteo), and warning thresholds (28°C and 32°C). It doesn't mention that it's read-only, but the concept of an outlook implies it only returns data; this is adequate for a no-parameter 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 a single bilingual sentence, about 25 words, that covers all the necessary facts without redundancy. It front-loads the purpose and adds precision.

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 tool is simple (no params, no output schema) and the description covers the essential information: what it computes and for which locations. It could be clearer on how the output is formatted (e.g., a list of temperatures), but this is a minor gap for such a straightforward 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?

With zero parameters, the schema is fully self-contained. The description adds value by explaining what the tool provides, which contextualizes the expected input (none).

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 explicitly states the tool's function: providing a live heatwave outlook for Germany over the next three days, with specific thresholds and cities. This is clearly distinct from sibling tools about building energy efficiency, so an agent can select it unambiguously.

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 context is clear: use this tool to retrieve a heatwave forecast for Berlin, Frankfurt, and Munich. However, unlike the get_calls example, it does not name alternative tools or specify when not to use it, so it lacks explicit usage boundaries.

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 burden. It transparently discloses the calculation method (the formula) and the output dimensions (hourly, daily, monthly costs). However, it does not mention edge cases, default values, or how results are returned, which would add completeness.

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 concise sentences, front-loaded with the German name and immediately followed by an English explanation. It conveys the essential information without waste, making it easy to parse quickly.

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 (a calculator with no nested objects or output schema), the description is fairly complete. It explains the purpose, formula, and reporting periods. It lacks only minor details like return format or potential pitfalls, but overall it is sufficient for correct invocation.

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 already provides 100% coverage with descriptions for all 5 parameters. The description adds value by explicitly linking the formula terms (watts, hours, price, duty cycle) to the parameters, reinforcing their meaning and how they combine. This enhances understanding beyond the schema alone.

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 calculates running costs for an air conditioner or any appliance, with the specific formula (watts × hours × price × duty cycle). This distinguishes it from sibling tools like btu_empfehlung or heizleistung_watt, which focus on heating/cooling capacity rather than cost.

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 makes it obvious when to use this tool: whenever you need to estimate electricity cost of an appliance. It mentions the relevant inputs and output time periods (per hour/day/month), though it does not explicitly state exclusions or alternatives. The context is clear enough for an agent to select it.

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 must disclose behavior. It states it returns the full plain text of one guide, implying a read-only operation. It adds context about the source (getecoback.com) and the intended use (citation), but does not cover error handling or HTTP behavior. This is adequate for a simple retrieval tool, but not exhaustive.

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 brief and bilingual, with each sentence providing distinct information: core function, purpose/context, and input source. No filler or redundancy, despite the language repetition.

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 tool is simple with one parameter and no output schema. The description covers the core behavior, input format, and intended use. It lacks explicit error behavior or return type specifics, but for a single-guide retrieval tool, it is fairly complete. A small gap exists around what happens for invalid paths.

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 describes 'pfad' as a path or full URL with an example. The description adds that the path/URL should come from ratgeber_suche, which is a key constraint not present in the schema. This enriches the parameter semantics.

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 function: 'Liefert den Volltext eines einzelnen Ratgebers als Klartext' and 'Returns the full plain text of one guide from getecoback.com'. It distinguishes itself from sibling ratgeber_suche by specifying it retrieves a single guide's full text, and even references ratgeber_suche as the source for paths.

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 instructs 'Pass a path or URL from ratgeber_suche', establishing a clear workflow: search first, then read. It also explains the purpose 'so the answer can be written from the source and cited'. This provides strong guidance on when and how to use the 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 provided, the description carries the full burden of disclosure. It transparently states that the tool searches a specific site and returns title, URL, and summary, but it does not disclose potential behaviors like rate limits, no-results handling, or whether it is a read-only operation (though this is inferable). This is sufficient for a simple search tool but lacks some detail.

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 core action and output, and the English translation adds bilingual accessibility without bloat. Every sentence earns its place, providing all essential information in a compact form.

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 and the absence of an output schema, the description adequately covers what the tool does, what it returns (title, URL, summary), and the domain of guides. It even adds the context of 'citable sources' for answer generation. It does not mention sorting or pagination, but these are not critical given the schema's 'max' parameter.

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 100% coverage with descriptions for all three parameters ('frage', 'max', 'sprache'), including examples. The description adds a topical context that could help formulate queries, but it does not introduce any new parameter-level meaning beyond what the schema already offers. Baseline 3 is appropriate.

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 function: 'Durchsucht die Ratgeber von getecoback.com' (searches this site's guides) and specifies the return fields: 'Titel, URL und Kurzbeschreibung' (title, URL, and summary). It distinguishes itself from sibling tools like 'ratgeber_lesen' (read guide) by explicitly using the verb 'durchsucht' (searches).

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 phrase 'citable sources for the answer' implies the tool is used to gather evidence for responses, and the topic list (air conditioning, window sealing, etc.) gives clear context for when it applies. However, it does not explicitly state when not to use it or mention alternatives like 'ratgeber_lesen', so it falls short of a 5.

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 full behavioral burden. It discloses the Magnus formula and the assumption that walls are calculated 2 °C below room temperature. It does not describe the output format, but these details add meaningful transparency beyond a simple 'calculates dew point'.

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 compact and front-loaded, with the German purpose followed by the translated/expanded English version. Both parts contribute: the first states the answer to 'should I ventilate?', and the second adds the formula and wall assumption.

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 three-parameter calculation tool with no output schema, the description provides enough context: inputs are fully documented in the schema, and the calculation logic is stated. It does not specify the return format, but this is a minor gap.

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%, so the baseline is 3. The description repeats the wall temperature assumption already present in the innen_temp_c parameter description and adds no new parameter-specific meaning.

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 computes the dew point of outside air and answers whether opening a window would make a basement or damp room wetter. This specific outcome distinguishes it from sibling tools like heating load or heat wave preview.

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 a clear use case: deciding if ventilating now would worsen dampness in a basement or damp room. It does not explicitly mention alternatives or exclusions, but the context is distinct enough from siblings.

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