hvac-btu-heat-klimaanlage

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 calculation basis ('Formel identisch mit dem Rechner auf getecoback.com (340 BTU/m², Sonnenfaktor)'), which gives insight into how the result is computed and the role of the 'sonne' parameter. It also indicates the output includes a device class. It does not reveal all edge cases or exact return structure, 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 concise and front-loaded: the main purpose is stated in the first phrase, and the second sentence adds the essential formula context. No wasted words; every element contributes to understanding the tool's function and calculation basis.

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 (2 parameters, no nested objects) and the description covers its core purpose, the formula, and the geographic scope. There is no output schema, but the description mentions the output includes 'passender Geräteklasse', partially covering return values. It could be more explicit about the exact output format (e.g., numeric BTU value and device class), but given the low complexity, 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?

The input schema already describes both parameters with 100% coverage, giving baseline 3. The description adds value by linking the parameters to the formula: '340 BTU/m²' clarifies the meaning of 'qm', and 'Sonnenfaktor' explains the 'sonne' parameter's role in the calculation. This enriches the parameter semantics beyond the schema's basic 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 clearly states the tool's function: 'Empfohlene Kühlleistung (BTU) für einen Raum' (recommended cooling capacity for a room) and also mentions it includes 'passender Geräteklasse' (appropriate device class). This distinguishes it from sibling tools like 'fensterabdichtung_laenge' or 'klimaanlage_stromkosten', which address different aspects of cooling/room sizing.

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 designed for rooms in Germany/Europe and follows the formula from getecoback.com. While it doesn't explicitly mention alternatives or when not to use it, the distinct purpose and sibling names make the appropriate usage clear. A small deduction for lack of explicit 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?

With no annotations provided, the description carries the full transparency burden. It discloses the calculation formula (perimeter = 2×(B+H)), the fact that it provides a matching standard size, and references an identical external calculator. This gives clear insight into the tool's behavior and output, though it does not explicitly state that it is read-only or describe any 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 exceptionally concise—two short sentences. It front-loads the main function, includes the formula in a compact form, and the second sentence about the web calculator is brief and informative. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple calculator with two required parameters and no output schema, the description adequately covers the return values (length and standard size) and the calculation logic. It does not explicitly state output units but implies they match inputs. This is sufficient given the low complexity and rich schema descriptions.

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 detailed descriptions for all parameters (100% coverage), including a helpful clarification that 'breite_cm' refers to the movable part, not the frame. The description adds semantic value by mapping the formula to the parameters and indicating how they relate to the calculation, elevating it above 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 clearly states the tool's purpose: computing the required length of a window seal for mobile air conditioners from the wing dimensions, including the formula (perimeter = 2×(B+H)) and the addition of a suitable standard size. This strongly distinguishes it from sibling tools (BTU recommendation, heat wave forecast, power costs), which address different aspects of air conditioning.

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 use when the user needs to determine seal length for mobile ACs, giving the context 'für mobile Klimaanlagen' and specifying inputs from wing dimensions. It does not explicitly mention alternatives or exclusions, but the siblings are clearly unrelated to this calculation, so the context is sufficient to guide selection.

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 transparency burden. It discloses the live nature, time window (next 3 days), city scope, data source (open-meteo), and classification thresholds (28°C/32°C). It does not mention update frequency or return format, but for a parameterless tool it provides meaningful behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, densely informative sentence. It puts the key purpose ('Live-Hitzevorschau') at the beginning and packs essential details (scope, timeframe, data source, thresholds) without any fluff or 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?

Given the tool's simplicity (no parameters, no output schema, no annotations), the description provides sufficient context: it explains what is covered, for which cities, over what period, and with what classification criteria. It could be slightly more explicit about the output structure (e.g., 'returns temperature values and a heat warning category'), but it is adequate for selection and 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 input schema has zero parameters, so the baseline is 4. There are no parameter details to explain, and the description's information about cities and time range effectively substitutes for parameter semantics. No additional parameter meaning is needed.

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+resource: 'Live-Hitzevorschau' for Germany, with concrete details (3-day max temperature, cities Berlin/Frankfurt/München, data source open-meteo). It clearly distinguishes itself from siblings like btu_empfehlung or klimaanlage_stromkosten, which address different heating/cooling aspects.

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 use: when a user needs a live heat preview for German cities over the next 3 days. It does not explicitly state exclusions or alternatives, but the specificity of the tool makes its intended usage obvious. A slight gap is the lack of a 'when not to use' note.

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 reveals the computation formula and references an external calculator for verification, which is useful. However, with no annotations provided, it does not disclose defaults (e.g., tage=30, auslastung=0.65) or the nature of the output (e.g., currency, rounding). It covers the core behavior but misses surrounding details.

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—two sentences that communicate the core formula and an external reference without redundant words. It is efficiently written, though it might have included a brief usage hint without much extra length.

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 and no annotations, the tool could be made more complete by stating the expected result, default values, and when to use it. The formula itself provides the core calculation logic, but the description lacks guidance on edge cases and practical usage context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% coverage of all five parameters with descriptions and defaults. The description only names the parameters in the formula without adding new meaning, so it stays at the baseline for schema-covered tools.

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 as calculating the electricity costs of an air conditioner, presenting the formula (Watt × hours × price × utilization). It distinguishes from siblings (e.g., BTU recommendation, window sealing) by specifying the exact scope and formula, though it lacks an explicit verb like 'berechnet'.

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 mention of when to use this tool versus sibling tools such as btu_empfehlung, fensterabdichtung_laenge, or hitzewelle_vorschau. The description simply states the formula and references an external site, without any conditional context or exclusions.

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