getecoback-raumklima

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

No annotations are provided, so the description carries the full burden. It discloses the tool's informational nature, what it covers (state programmes, payback arithmetic) and explicitly states what it does not cover ('No federal purchase premium — only the VAT exemption'). This gives a clear behavioral profile without 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 compact and front-loaded, starting with the main topic and key details. It includes both German and English explanations, which adds a bit of redundancy but remains efficient. There is no wasted prose, and it effectively uses two sentences to convey substantial 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?

For a tool with 4 optional parameters and no output schema, the description is highly complete: it explains the subject matter, the scope, exclusions, and hints at the calculation feature. Combined with the comprehensive schema, it gives the agent enough context to select and use the tool correctly.

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 each parameter clearly described in the input schema. The description reinforces the payback context (e.g., ~100€ bonus) but does not add significant detail beyond the schema about individual parameters. Given high schema coverage, the baseline of 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 uses a specific verb ('Förderung... und wie ein Zuschuss die Amortisation verkürzt') and resource ('Balkonkraftwerk-/Speicher-Förderung in Deutschland'), clearly stating what the tool does: informs about state subsidies and calculates payback impact. It distinguishes itself from siblings by focusing on subsidies and payback arithmetic, which none of the sibling tools cover.

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?

Provides clear context by specifying the geographical scope (Germany), the types of subsidies (state programmes, ~100€ storage bonus, VAT exemption), and exclusions (no federal purchase premium). While not explicitly naming alternative tools, the scope is well-defined so an agent can infer when this tool is appropriate versus the sibling 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 carries the full burden for behavioral disclosure. It discloses the calculation formula and regional applicability, which adds transparency. However, it does not describe the output format, device class details, or limitations (e.g., ceiling height assumptions), leaving gaps for a tool with no annotation support.

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 compact sentences, one in German and one in English, front-load the purpose and immediately provide the formula and scope. There is no filler or repetition, and every sentence 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 2-parameter tool with no output schema, the description covers purpose, formula, and geographic scope. It is nearly complete but omits what the response looks like (e.g., plain text vs structured JSON) and the exact device class classification, which would be useful for a fully self-contained description.

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%, providing a baseline of 3. The description adds value by explaining the relationship between qm and sonne via the sun factor formula, and clarifies that sonne modifies the multiplier, which goes beyond the schema's simple field 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 uses a specific verb phrase 'Empfohlene Kühlleistung (BTU) für einen Raum' and clearly states the output includes a matching device class. It poses a concrete question ('how many BTU do I need for X m²?') and distinguishes itself from sibling tools like heating (heizleistung_watt) and cost calculators (klimaanlage_stromkosten).

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: it is for calculating cooling capacity in rooms, with an explicit formula (340 BTU/m² × sun factor) and geographic scope (Germany/Europe). It implicitly differentiates from siblings by focusing on cooling, but does not explicitly state when not to use it or name alternatives.

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 behavioral traits, and it does convey the core calculation (perimeter formula) and output (length plus off-the-shelf size). However, it claims to cover only 'tilt-and-turn and roof windows' while the schema includes 'kipp' (tilt-only) as the default type, which is an incomplete and potentially misleading coverage statement. This gap in transparency is significant for a tool with 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?

The description is concise, with two sentences in German and English that pack all essential information: purpose, formula, output, and supported window types. It is front-loaded and every phrase contributes, avoiding fluff or redundancy beyond the intentional bilingual presentation.

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 moderate complexity (3 params, no output schema, no annotations), the description covers the main aspects: what it calculates, how (formula), and what it returns (length and off-the-shelf size). It could be more complete by stating the units of the output or explicitly listing all supported window types, but it is still sufficiently complete for a straightforward calculation 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 description coverage is 100%, so the baseline is 3. The description adds value beyond the schema by explaining that the length is derived from the sash perimeter (2×(width+height)), reinforcing that 'breite' is the movable part (as schema already notes) and that 'fenstertyp' influences the available coverage. This clarifies how parameters relate to the computation.

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 the required window-seal length from sash measurements, provides the perimeter formula (2×(width+height)), and indicates it also returns the off-the-shelf size that fits. This specific verb+resource distinguishes it from siblings like btu_empfehlung or heizleistung_watt, which address other aspects of portable AC selection.

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: it is for portable air conditioners, based on sash measurements, and covers specific window types. It does not explicitly mention when not to use the tool or alternatives, but the context is sufficient since the sibling tools serve clearly different purposes (BTU, power, costs, guides, etc.). A minor omission is that it does not mention the 'kipp' (tilt-only) window type despite its default in the schema.

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?

There are no annotations, so the description carries the full burden. It discloses that the tool combines physical reasoning ('honest physics'), room sizing, and a guide selection, and indicates it is a decision/recommendation layer rather than a precise calculator. It does not detail the exact output shape, but for a non-mutating selector this is adequate.

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 carry the core purpose, inputs, output style, and sibling-tool hierarchy with no filler. The German/English pairing also aids multilingual agent parsing without bloating the text.

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 only two parameters, no output schema, and no annotations, the description is nearly complete: it explains the tool's role, its problem domain, and its output (device family plus matching guide). It could be more explicit about edge-case behavior, but the core information an agent needs to invoke it correctly is present.

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 schema already explains both 'qm' and 'problem' with enums and units. The description reinforces the mapping by mentioning the problem categories and room size, but adds no parameter-level 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 opens with a concrete selection question and names the resource: 'device family' for an indoor-climate problem. It lists the problem categories and positions itself as 'the decision layer above btu_empfehlung/heizleistung_watt', which clearly distinguishes it from the closest sibling 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 phrase 'The decision layer above btu_empfehlung/heizleistung_watt' gives clear contextual guidance: use this tool first to choose a device family, then use the sizing tools. It does not explicitly enumerate when-not-to-use conditions or alternatives like ratgeber_suche, but the hierarchy provides solid routing 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?

With no annotations, the description discloses the calculation method (60/80/100 W/m²), scope (infrared/electric), and that running cost per full-load hour is included, offering meaningful behavioral context beyond a simple statement.

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 concise sentences (one bilingual) convey the core purpose and key calculation parameters without redundancy or unnecessary 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 tool with 3 parameters and no output schema, the description effectively communicates inputs, calculation basis, and that both power and cost are output, though it does not specify the exact return format or note any limitations.

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 descriptions already cover all parameters (100%), but the description clarifies the purpose of the electricity price parameter by linking it to running cost per full-load hour, and the insulation mapping is already in the schema; the added context about output slightly exceeds the baseline.

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 calculates required heating power in watts for a room based on floor area and insulation standard, distinguishing it from siblings like BTU recommendation or AC cost 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?

It implies usage for heating power estimation but does not provide explicit guidance on when to choose this over alternative tools such as btu_empfehlung, nor does it state any exclusions.

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 burden. It discloses the data source (open-meteo), the cities, and threshold flags, but does not explain what 'flagged' means in terms of output or any limits. This is partial 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 two concise sentences, with the German and English versions side by side, and every detail (time range, cities, data source, thresholds) is relevant. No fluff.

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 no output schema, the description is fairly complete: it states the purpose, scope, data source, and thresholds. However, it could clarify what the output format looks like and what 'flagged' specifically means.

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 adds value by specifying the scope (Germany, three cities) and the data source, which provides context 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 a live heatwave outlook for Germany, specifying the metric (highest temperature), the time range (next three days), and the cities covered (Berlin, Frankfurt, Munich), which distinguishes it from the sibling tools that focus on home energy advice.

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 implicitly defines when to use the tool—when a heatwave forecast for major German cities is needed—but does not explicitly mention alternatives or exclusions. The context is clear, though no direct comparison to sibling tools is provided.

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 disclosure burden. It explains the calculation formula (watts × hours × price × duty cycle) and the outputs (cost per hour/day/month). This is sufficient for a pure calculation tool with no side effects, though it doesn't mention defaults or edge cases.

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 front-loaded, with the key concept in the first sentence. It includes the formula and output periods without fluff. Some redundancy exists due to bilingual repetition, but it remains efficient and readable.

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 calculator with no output schema and no annotations, the description is complete: it states the formula, inputs, and outputs. It gives enough context for an agent to invoke correctly and interpret results without needing further explanation.

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 baseline is 3. The description adds value by linking parameters to the formula—identifying auslastung as compressor duty cycle and clarifying that elapsed time is hours per day. This goes beyond the schema's individual 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 a specific purpose: calculating running costs for an air conditioner or any appliance, with a formula. It distinguishes itself from sibling tools like heizleistung_watt and btu_empfehlung, which address other HVAC 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 description provides clear context on when to use the tool (to determine electricity costs of running an appliance) and the core calculation approach. It doesn't explicitly exclude alternatives or compare with siblings, but the intent is unambiguous given the sibling tools are about sizing/heat, not costs.

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 burden. It discloses the output (full plain text), the source (getecoback.com), and the intended use (writing answers with citation). It does not mention error handling, rate limits, or output size, but for a simple read operation 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 sentences, each serving a purpose: the German sentence states the primary function, and the English sentence adds usage guidance and rationale. It's concise and well-structured, though slightly redundant in echoing the German content.

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 has only one parameter and no output schema, but the description compensates by defining the return type ('Klartext' / plain text) and the source. It also links to the companion search tool, making the usage context complete for a simple retrieval 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 schema covers 100% of the parameter with a detailed description of 'pfad'. The description adds extra context by specifying that the path should come from ratgeber_suche, which is useful for correct invocation.

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' (Returns the full plain text of one guide). It distinguishes itself from siblings like ratgeber_suche by specifying that it retrieves the full content of a single guide, not metadata or search results.

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 provides explicit workflow guidance: 'Pass a path or URL from ratgeber_suche' indicates when to use this tool after searching. It also explains the purpose ('so the answer can be written from the source and cited'), clarifying the intended context. However, it doesn't explicitly state when not to use it or name alternatives beyond ratgeber_suche.

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 discloses the search scope, topics, and return fields (title, URL, summary). However, it does not mention potential limitations, rate limits, or the read-only nature, which could be assumed but not explicitly 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?

The description is efficient and front-loaded with the main action. It is slightly verbose due to bilingual repetition (German and English), but the extra sentence about 'citable sources' adds value. Overall, it is well-structured.

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 schema covers parameters fully and there is no output schema, the description adequately explains what the tool returns (title, URL, summary per match) and the topics covered. It could be more explicit about the result format (e.g., list of objects), but it is sufficient for most use cases.

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 100% coverage for all parameters, including descriptions and examples. The tool description adds no additional semantic detail beyond what the schema offers, so the 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 searches getecoback.com guides on specific topics (air conditioning, window sealing, etc.) and returns title, URL, and summary. It differentiates from siblings by emphasizing 'citable sources for the answer', which distinguishes it from ratgeber_lesen (likely for reading content).

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 (when needing to find guides/sources on the listed topics) and provides clear context. However, it does not explicitly mention alternatives or when not to use it, 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 available, the description carries the full burden. It discloses the use of the Magnus formula and the key assumption that walls are counted 2 °C below room temperature. This gives the user insight into the calculation method. It does not specify output format or edge cases, but the description implies a recommendation (yes/no) which is adequate for a simple calculation 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 concise, with two sentences, and front-loads the core purpose. The bilingual repetition adds a little redundancy but does not significantly inflate length. No unnecessary filler is present.

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 only three inputs and no output schema. The description explains the exact scenario (damp room/basement ventilation) and the calculation assumption. It adequately covers what the user needs to know, though it could optionally mention the output type (e.g., 'yes/no recommendation') more explicitly.

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?

All three parameters are fully described in the schema (100% coverage). The description adds context about the formula and wall assumption, but this is mostly restating what is already in the schema (e.g., walls counted 2 °C cooler). Since schema coverage is high, the baseline of 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: it computes the dew point of outside air and answers whether opening a window would make a basement or damp room wetter. It uses specific terminology (dew point, Magnus formula) and distinguishes itself from sibling tools like BTU calculations or window sealing by focusing on ventilation decisions.

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: this is for deciding whether to ventilate a basement or damp room based on outdoor conditions. It implies when to use it, but does not explicitly mention when not to use it or compare with alternatives. Since the sibling tools are all different domains (heating, sealing, etc.), the context is sufficient to avoid confusion.

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