getecoback-climate-weather

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

With no annotations, the description must carry the transparency burden. It discloses the formula (340 BTU/m², Sonnenfaktor) and that it mirrors a known calculator, which is useful. However, it does not mention limitations, error behavior, what 'passender Geräteklasse' entails, or the output format. Some behavioral context is present but incomplete.

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 long, front-loads the core purpose, and includes a reference to the formula without unnecessary detail. Every word contributes to understanding, and the structure is clear and 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?

The tool is relatively simple (2 params, no output schema), and the description covers the essential purpose and formula. It also mentions the device class output implicitly. It could be more complete by describing the output structure, but for the complexity level, it is sufficiently complete for an agent to select and use the 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%, so baseline is 3. The description adds value by explaining the formula constants (340 BTU/m² and Sonnenfaktor), which clarifies how qm and sonne interact. This goes beyond the simple field descriptions and aids understanding of the calculation 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 purpose: 'Empfohlene Kühlleistung (BTU) für einen Raum in Deutschland/Europa' specifies the resource (cooling capacity for a room) and context (Germany/Europe). It also distinguishes itself from siblings like fensterabdichtung_laenge and klimaanlage_stromkosten by focusing on BTU recommendation and device class.

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 (for estimating BTU cooling capacity based on room size and sun exposure) but does not explicitly mention alternatives or exclusions. It references an identical calculator formula, which provides context, but lacks explicit 'use this instead of X' guidance relative to 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?

The description discloses the core calculation formula (Umfang = 2×(B+H)) and states the result is identical to the web calculator, providing useful behavioral context. However, it omits the effect of the fenstertyp parameter and the output format/unit, and with no annotations the description carries the full transparency 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 two short sentences, front-loaded with the purpose. The second sentence ('Identisch mit dem Rechner...') adds a behavioral anchor but is slightly redundant. It is concise and well-structured, though it could be more direct with an explicit verb.

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, the description covers the main computation and the existence of a standard size, and the schema handles parameter details. However, with no output schema, the description should clarify the return value and the role of fenstertyp; these are notable gaps.

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 provides full descriptions (100% coverage) for all parameters, including ranges and clarification that breite_cm is the movable part. The description's formula maps B and H to breite and hoehe but adds no new parameter-specific detail, and the optional fenstertyp is not mentioned in the description.

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, plus a standard size. It distinguishes from sibling tools (BTU recommendation, heat wave preview, electricity costs) by focusing on seal length, but it is phrased as a noun phrase rather than with a specific 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?

The context is implied: use when you have wing dimensions for a mobile AC window seal. The sibling tools clearly cover different functions, but the description does not explicitly mention when to use this tool vs. alternatives or exclude other scenarios, nor does it address the optional fenstertyp parameter.

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 data source (open-meteo) and mentions classification thresholds (28°C/32°C), which adds some behavioral context. However, it does not explain what 'Einordnung' means precisely, nor what the output format is, and there are no annotations to fall back on.

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, efficient sentence that packs all key information: purpose, geography, time frame, data source, and thresholds. 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?

While the description is clear for a simple tool, it lacks explicit details about the return value or classification semantics (what exactly 'Einordnung' means). Without an output schema, the agent is left to infer the response structure.

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 is empty. The baseline of 4 applies since no parameter descriptions are needed. The description adds no parameter semantics, but that is acceptable given the parameterless design.

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 for specific cities (Berlin, Frankfurt, Munich). It distinguishes itself from sibling tools by focusing on weather/heat rather than building 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 gives clear context: use for heat previews in Germany with a 3-day horizon and specific cities. It implies when to use (when asked about upcoming heat), though it does not explicitly name alternatives or 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?

Ohne Annotationen trägt die Beschreibung die volle Verantwortung. Sie offenbart die Kernformel (Watt × Stunden × Strompreis × Kompressor-Auslastung) und erklärt damit die Berechnungslogik. Allerdings fehlt die Erwähnung der Multiplikation mit der Tagesanzahl, was zu Unklarheiten führen kann.

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?

Die Beschreibung ist mit zwei Sätzen sehr knapp und fokussiert. Jeder Satz trägt bei, aber die Formel könnte präziser sein, indem auch 'tage' erwähnt wird. Insgesamt gut strukturiert und leicht verständlich.

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?

Für einen einfachen Rechner fehlen Angaben zum Rückgabewert und die vollständige Einbeziehung aller Parameter (Tage). Da kein Output-Schema existiert und keine Annotationen vorliegen, sollte die Beschreibung stärker auf Vollständigkeit achten, bleibt aber noch angemessen.

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?

Das Schema beschreibt alle 5 Parameter vollständig (100% Abdeckung), daher ist der Baseline-Wert 3. Die Beschreibung fügt die Beziehung der Parameter hinzu, lässt aber 'tage' in der Formel aus, was potenziell irreführend ist und den semantischen Mehrwert mindert.

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?

Die Beschreibung sagt klar, was das Tool tut: 'Stromkosten eines Klimageräts' und nennt die Formel. Sie unterscheidet sich eindeutig von den Geschwistern (BTU-Empfehlung, Fensterabdichtung, Hitzewelle), die andere Aspekte abdecken.

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?

Die Beschreibung liefert einen klaren Kontext (Berechnung der Stromkosten einer Klimaanlage), nennt aber keine expliziten Alternativen oder Ausschlusskriterien. Der Anwendungsfall ist klar erkennbar, aber eine Abgrenzung zu den Schwester-Tools fehlt.

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