getecoback-raumklima

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 calculation formula (340 BTU/m², sun factor) and that it returns a device class, giving insight into the tool's behavior. It does not describe output format, but for a non-mutating calculation 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?

Two concise sentences, front-loaded with the core purpose, then the formula reference. Every word earns its place with no filler or repetition of schema details.

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

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

Given the tool's simplicity (2 params, 1 required) and lack of output schema, the description adequately covers what the tool does (BTU recommendation + device class) and its scope. It does not specify return format, but the stated outputs are clear enough for a basic 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 coverage is 100% (both 'qm' and 'sonne' are described in the schema). The description adds extra meaning by stating the formula constant (340 BTU/m²) and how 'sonne' (sun factor) modifies the calculation, going beyond the schema's simple 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 states a specific verb+resource: 'Empfohlene Kühlleistung (BTU) für einen Raum' (recommended cooling capacity for a room) and adds the device class. It clearly distinguishes from siblings like 'klimaanlage_stromkosten' which deals with costs, and 'fensterabdichtung_laenge' which is about window sealing.

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 rooms in Germany/Europe with a specific formula and sun factor. It implies when to use (when BTU recommendation is needed) but does not explicitly mention alternatives or exclusions. The scope is well-defined enough to guide appropriate use.

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 discloses the calculation formula and the fact that it also outputs a confectioned size. However, with no annotations, it does not cover edge cases, input limits, or the exact return format. This leaves some uncertainty for a simple calculator 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 (two sentences) and front-loaded with the main purpose, followed by the formula and a reference to an existing calculator. No unnecessary words 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?

The tool is simple, and the description covers its core functionality. It does not explicitly describe the output structure, but the mention of 'passende Konfektionsgröße' implies the output. Given the low complexity, this 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 coverage is 100%, so the baseline is 3. The description adds value by explicitly relating breite_cm and hoehe_cm to the circumference formula, giving semantic meaning beyond the schema's type/range 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 computes the required window seal length for mobile ACs from wing dimensions using the formula Umfang = 2×(B+H), and also provides the matching confectioned size. This is distinct from sibling tools like BTU recommendation or electricity cost calculators.

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: it is for window seal length calculation for mobile ACs. It does not explicitly mention when not to use it or name alternatives, but the purpose is specific enough that an agent can infer applicability.

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 burden. It discloses that the data is live, sourced from open-meteo, and limited to Berlin, Frankfurt, and Munich. It also mentions classification thresholds, but it does not discuss error behavior, rate limits, or the exact return format.

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, compact sentence that packs essential information: topic, region, time range, cities, data source, and thresholds. No unnecessary words or 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 parameterless tool, the description fully explains the purpose and likely output: max temperatures for 3 days and a heat classification. Although no output schema is provided, the description sufficiently conveys what the agent can expect.

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% (vacuously). The description adds no parameter info because none is needed, and the baseline for 0 parameters is 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states what the tool does: provides a live heat preview for Germany with maximum temperatures for the next 3 days in specific cities. It also includes classification thresholds, distinguishing it from sibling tools that cover BTU recommendations, window sealing, and AC costs.

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 heat preview) and gives specific context (cities, time range, thresholds). However, it does not explicitly mention when not to use it or point to alternatives, leaving the agent to infer usage from the sibling names.

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 discloses the core calculation formula (Watt × Stunden × Strompreis × Kompressor-Auslastung), which is a meaningful behavioral detail. However, it omits the role of the 'tage' parameter and does not describe the output format or any edge cases. Since no annotations are present, the description only partially carries the 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?

Two short sentences with the purpose front-loaded and no unnecessary words. The reference to the website adds minimal value but does not bloat the description.

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 five parameters and no output schema, the description is too sparse. It fails to explain what the return value represents (e.g., monthly or daily costs), how 'tage' factors into the calculation, and the units of the result. Missing these details leaves an agent uncertain about 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?

The formula in the description adds relational meaning to the parameters, showing how watt, hours, price, and utilization combine. This goes beyond the individual schema descriptions. However, the formula is incomplete (it omits 'tage' and any conversion to kWh) and could mislead, so it does not fully elevate 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 identifies the tool as computing 'Stromkosten eines Klimageräts' (electricity costs of an AC unit) and provides the formula, which distinguishes it from sibling tools like BTU recommendation or heatwave preview. However, it lacks an explicit verb like 'calculate' and relies on the noun phrase, so it is not a perfect 5.

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?

There is no guidance on when to use this tool or how it compares to alternatives. The only extra context is a reference to 'getecoback.com' which does not help an agent select this tool. No exclusions or alternative tool mentions are provided.

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