BTU sizing, window-seal length, heatwave outlook, running costs, balcony solar subsidies (DE/EU).
Balkonkraftwerk-/Speicher-Förderung in Deutschland (Stand 08/2026) und wie ein Zuschuss die Amortisation verkürzt. — German subsidies for plug-in balcony solar and storage: which state programmes exist, the ~100 € storage bonus, the apply-BEFORE-buying rule most programmes enforce, and the payback arithmetic with and without a grant. No federal purchase premium — only the VAT exemption.
| Name | Required | Description | Default |
|---|---|---|---|
| preis_eur | No | Kaufpreis des Speichers/Sets in € für die Amortisationsrechnung (optional) | |
| bundesland | No | Bundesland, z. B. 'Sachsen' oder 'Berlin' — German federal state (optional; ohne Angabe wird die Gesamtlage beschrieben) | |
| zuschuss_eur | No | Erwarteter Zuschuss in € (optional, Default 0) | |
| ersparnis_eur_jahr | No | Jährliche Stromersparnis in € (optional, Default 100 — typisch 60–120 € bei 1–1,5 kWh/Tag Verschiebung) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior. It mentions the payback arithmetic and factual scope (e.g., 'No federal purchase premium'), but it does not explicitly describe how the tool processes parameters or what outputs to expect. The temporal 'Stand 08/2026' and the apply-before-buying rule add useful 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 concise two-sentence structure with a German and English version. It front-loads the topic and packs measurable value without excessive fluff. The inclusion of a data freshness date is useful.
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 and lack of output schema, the description is reasonably complete for an informational tool. It conveys the main topics and the key rule (apply-before-buying), but it does not explicitly state the output format or all edge-case behaviors (e.g., invalid bundesland, missing parameters).
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% for all four parameters, so the baseline is 3. The description adds some context by referencing state programmes (bundesland) and payback arithmetic (zuschuss_eur/ersparnis_eur_jahr), but it does not go beyond what the schema already explains.
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 covers German subsidies for balcony solar and storage, including state programmes, the storage bonus, the apply-before-buying rule, and payback arithmetic. This is a specific resource and clearly distinguishes it from siblings like btu_empfehlung or 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 implies usage by listing the covered topics, but it lacks explicit guidance on when to use this tool versus alternatives or when not to use it. No sibling tools are mentioned, and there are no exclusions or prerequisite conditions given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Empfohlene Kühlleistung (BTU) für einen Raum, mit passender Geräteklasse. — Recommended cooling capacity in BTU for a room, with the matching device class: how many BTU do I need for X m²? Same formula as the calculator on getecoback.com (340 BTU/m² × sun factor), for Germany and Europe.
| Name | Required | Description | Default |
|---|---|---|---|
| qm | Yes | Raumfläche in m² — room floor area in square metres (4–120) | |
| sonne | No | Sonneneinstrahlung — sun exposure: wenig = low/shaded, normal, viel = strong (south/west or top floor). Default: normal |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the calculation formula (340 BTU/m² × sun factor), the regional scope, and the output (BTU plus device class). This goes beyond a simple 'calculates BTU' and helps the agent understand internal logic, though it doesn't specify exact output formatting or error handling.
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, containing only two main clauses, and is front-loaded with the core purpose. The bilingual repetition (German and English) is slightly redundant but not wasteful; it ensures clarity for a wider audience while keeping the description compact.
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 two-parameter calculation tool with no output schema and no annotations, the description is fairly complete. It explains what is returned (BTU and device class), the formula, and the target region. It doesn't describe the exact return data structure, but the simplicity of the tool makes this less critical.
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 covers 100% of parameters with descriptions, but the description adds the formula (340 BTU/m² × sun factor), which clarifies how 'qm' and 'sonne' are used in the calculation. This extra semantic detail enriches understanding beyond the bare 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's function: recommending cooling capacity in BTU for a room, including the matching device class. It uses specific verbs ('recommended', 'how many BTU do I need') and distinct resource ('cooling capacity in BTU for a room'), which distinguishes it from sibling tools like heizleistung_watt (heating) and klimaanlage_stromkosten (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 gives an explicit usage scenario ('how many BTU do I need for X m²?') and specifies the target region (Germany/Europe). It doesn't name alternative tools, but the uniqueness of the cooling capacity calculation is clear enough for an agent to select this tool over its siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Benötigte Länge einer Fensterabdichtung für mobile Klimaanlagen aus den Flügelmaßen. — Required window-seal length for a portable air conditioner from the sash measurements (perimeter = 2×(width+height)), plus the off-the-shelf size that fits. Covers tilt-and-turn and roof windows.
| Name | Required | Description | Default |
|---|---|---|---|
| hoehe_cm | Yes | Flügelhöhe in cm (20–300) | |
| breite_cm | Yes | Flügelbreite in cm (20–300) — der bewegliche Teil, nicht der Rahmen | |
| fenstertyp | No | Fenstertyp — window type: kipp/drehkipp = tilt or tilt-and-turn, dachfenster = roof/skylight. Default: kipp |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It does disclose the calculation formula and the two-part output (required length and standard size), which is useful. However, it does not mention edge cases, input range limits, or potential error behavior, which is a gap given the lack of annotation 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 two sentences, front-loaded with the core purpose, and includes the formula and scope without redundancy. Every clause contributes useful information, making it highly 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?
Given there is no output schema, the description adequately explains the return concept: required length plus a fitting standard size. It also includes the formula and supported window types. Missing exact return format (e.g., units or combined output structure) but acceptable for a simple 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 baseline is 3. The description adds value by linking width and height to the perimeter formula, reinforcing their meaning as sash measurements. It also covers the fenstertyp parameter by stating the supported window types, which aligns with the enum values.
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 the required window-seal length for a portable AC from sash dimensions, with the formula (perimeter = 2×(width+height)), which is specific and distinguishes it from sibling tools like btu_empfehlung or klimaanlage_stromkosten. It also mentions it returns an off-the-shelf size, further defining its purpose.
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 the seal length for a portable AC given window sash measurements. It also specifies covered window types (tilt-and-turn and roof windows), providing scope guidance. However, it does not explicitly mention alternatives or when not to use it, though the siblings are clearly different domains.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Welches Gerät löst mein Raumklima-Problem? — Which device family solves a given indoor-climate problem (too hot, damp/mould, too cold, stale air), with the honest physics, the right size for the room and the matching guide. The decision layer above btu_empfehlung/heizleistung_watt.
| Name | Required | Description | Default |
|---|---|---|---|
| qm | No | Raumfläche in m² — room floor area in square metres (4–120). Default: 20 | |
| problem | Yes | Das Problem — the problem: zu_heiss = room too hot, feucht_schimmel = damp air / condensation / mould risk, zu_kalt = room too cold (no fixed heating), stickige_luft = stale air / odours |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral disclosure burden. It communicates that this is an advisory/decision tool returning a device family, sizing, and a matching guide, and it adds an honesty expectation via 'honest physics'. However, it does not describe output format, assumptions, or possible side effects, which leaves some behavioral gaps.
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?
A single bilingual sentence packs purpose, scope, quality promise, and sibling relations efficiently. Some redundancy exists because the first clause is essentially repeated in English, and 'honest physics' is slightly vague, but the description remains economical and front-loaded.
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 2-parameter tool with complete schema descriptions and no output schema, the description names expected outcomes (device family, size, matching guide) and situates itself among related tools. It does not specify the exact return format or how the matching guide is provided, but this is a minor gap for a decision-layer recommendation 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 schema already documents the problem enum and the qm unit, range, and default. The description's mention of 'the right size for the room' reinforces qm's role but adds no concrete semantic detail beyond the schema. 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 states a concrete decision task ('Which device family solves a given indoor-climate problem'), lists the specific problem categories it covers, and explicitly distinguishes it from sibling tools by calling it 'the decision layer above btu_empfehlung/heizleistung_watt'. This makes the tool's purpose clear and differentiated.
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 'decision layer above btu_empfehlung/heizleistung_watt' gives useful navigational context, implying this tool is the family-level recommendation step before detailed calculation tools. It does not explicitly state when not to use it or name alternative conditions, so it stops just short of full exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Benötigte Heizleistung in Watt für einen Raum (Infrarot/Elektro). — Required heating power in watts for a room, from floor area and insulation standard (60/80/100 W/m² for new build, existing, old building), including running cost per full-load hour.
| Name | Required | Description | Default |
|---|---|---|---|
| qm | Yes | Raumfläche in m² (1–100) | |
| daemmung | No | Dämmstandard: gut = Neubau (60 W/m²), mittel = Bestand (80), schlecht = Altbau (100). Default: mittel | |
| strompreis_euro_kwh | No | Arbeitspreis in €/kWh für die Betriebskosten (Default: 0.30) |
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 does disclose the calculation basis (60/80/100 W/m²) and the inclusion of running cost, but it does not mention potential assumptions (e.g., standard room height) or limitations of the estimate. This is moderate transparency for a 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 compact, front-loading the core purpose, and includes useful detail in two sentences. The bilingual repetition (German and English) introduces slight redundancy but does not detract significantly from conciseness.
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 or annotations, the description covers the essential inputs, the calculation basis, and the output components (watts and running cost). It omits fine details like output formatting or caveats, but given the low complexity, it is reasonably 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 provides full descriptions for all three parameters, including the W/m² mapping for the enum. The description adds little beyond restating that floor area and insulation are inputs, so it does not significantly enhance parameter understanding beyond the schema's high coverage.
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 required heating power in watts for a room based on floor area and insulation standard. It also specifies the inclusion of running cost per full-load hour, which distinguishes it from sibling tools like btu_empfehlung that likely focus on BTU units.
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 a user needs heating power in watts with floor area and insulation data, but it does not explicitly mention when to use this tool over alternatives (e.g., btu_empfehlung) or any exclusions. No direct guidance or alternative references are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Live-Hitzevorschau für Deutschland (nächste 3 Tage). — Live heatwave outlook for Germany: highest temperature over the next three days across Berlin, Frankfurt and Munich (open-meteo), flagged from 28 °C and 32 °C.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden. It discloses the data source (open-meteo), geographic scope (Berlin, Frankfurt, Munich), time horizon (next 3 days), and threshold flags (28°C and 32°C), making behavior transparent. It doesn't mention potential latency or failure modes, but for a parameterless lookup 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?
A single bilingual sentence packs all essential information without redundancy, making it efficient and 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 tool has no parameters, no annotations, and no output schema, the description covers the key aspects: what, where, when, and how the data is sourced. It effectively tells the user what to 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?
There are no parameters, so the description doesn't need to explain any. The baseline score for zero parameters is 4, and the description's mention of the output scope is sufficient.
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 provides a live heatwave outlook for Germany with specific cities, time range, and temperature thresholds, distinguishing it from the unrelated 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 use case is implied: any time a user asks for a heatwave forecast for major German cities over the next three days. It does not explicitly name alternatives, but the sibling tools are clearly unrelated, so context alone is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Stromkosten eines Klimageräts. — Running cost of an air conditioner or any appliance: watts × hours × electricity price × compressor duty cycle. What does it cost to run per hour, per day, per month?
| Name | Required | Description | Default |
|---|---|---|---|
| tage | No | Anzahl Tage (Default: 30) | |
| watt | Yes | Leistungsaufnahme in Watt (z. B. 1000) | |
| auslastung | No | Kompressor-Auslastung 0–1 (Default: 0.65) | |
| stunden_pro_tag | Yes | Betriebsstunden pro Tag | |
| strompreis_euro_kwh | Yes | Arbeitspreis in €/kWh (z. B. 0.30) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the burden of behavioral disclosure. It transparently explains the calculation logic—watts, hours, price, and compressor duty cycle—and the time periods covered. It does not mention output formatting or edge cases, but for a simple calculator the formula and period breakdown provide adequate 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 extremely concise—two short sentences—with the German purpose front-loaded and the English expansion immediately after. Every phrase adds value: the formula, the scope, and the output periods. There is no redundant or filler 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?
For a simple calculator with fully documented schema parameters and no output schema, the description is complete enough: it explains what the tool does, the formula, and the output units/periods. There is no significant missing information that would prevent an agent from selecting and invoking 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%, so each parameter is already documented with units and defaults. The description adds semantic value by showing how the parameters combine in the formula (watts × hours × price × duty cycle), which clarifies the relationship between them beyond the individual schema 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 calculates running costs ('Stromkosten eines Klimageräts'/'Running cost of an air conditioner'), provides the formula (watts × hours × electricity price × compressor duty cycle), and specifies the output question (per hour/day/month). This distinguishes it from sibling tools like btu_empfehlung or heizleistung_watt, which address sizing or heating power 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 gives clear context: use this tool when you need to know what it costs to run an appliance, with explicit output periods (per hour, day, month). It does not explicitly name alternative tools or exclusions, but the purpose is distinct enough from sibling tools that usage context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Liefert den Volltext eines einzelnen Ratgebers als Klartext. — Returns the full plain text of one guide from getecoback.com so the answer can be written from the source and cited. Pass a path or URL from ratgeber_suche.
| Name | Required | Description | Default |
|---|---|---|---|
| pfad | Yes | Pfad oder vollständige URL, z. B. /guide/klimaanlage-kippfenster.html |
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 tool returns full plain text and that it's from getecoback.com, which implies a read-only, non-destructive operation. However, it does not mention error behavior or the potential size of the text, leaving a minor gap in 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 short sentences, one in German and one in English. It is front-loaded with the core function and immediately explains usage and source. No wasted words; every sentence contributes to clarity.
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 one-parameter tool with no output schema or annotations, the description is complete: it states the return value (full plain text), the source (getecoback.com), and the input requirement (path/URL from ratgeber_suche). It sufficiently covers both input and output semantics.
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 specifying that the path or URL should come from ratgeber_suche, providing source context beyond the schema's example. This helpful guidance raises the score to 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 that the tool delivers the full plain text of a single guide, using the specific verb 'returns' and the resource 'full plain text of one guide' from getecoback.com. It distinguishes itself from sibling tools like ratgeber_suche by focusing on a single guide and explicitly referencing the search tool as the source of the path/URL.
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 to pass a path or URL from ratgeber_suche, providing a clear prerequisite and context for when to use this tool. It also states the purpose: to write the answer from the source and cite it, effectively implicitly contrasting with the search tool that returns summaries or lists.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Durchsucht die Ratgeber von getecoback.com und gibt Titel, URL und Kurzbeschreibung zurück. — Searches this site's guides on air conditioning, window sealing, ventilation, heating, dehumidifiers and electricity costs, returning title, URL and summary for each match — citable sources for the answer.
| Name | Required | Description | Default |
|---|---|---|---|
| max | No | Anzahl Treffer (1–10, Default: 5) | |
| frage | Yes | Suchbegriff oder Frage — search term or question, German or English, e.g. 'Klimaanlage Kippfenster abdichten' or 'portable ac tilt window' | |
| sprache | No | Nur deutsche oder nur englische Seiten (Default: beide) |
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 return fields (title, URL, summary) and topic scope, which is helpful, but it doesn't specify how search works (e.g., full-text vs title-only), result ordering, or any limits beyond those in the schema. The read-only nature is implied 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 a single sentence with a bilingual translation. It is brief and front-loaded, but the English translation is redundant for an agent that can handle German. Still, it is efficient and easy to parse.
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 adequately covers return values and topic scope. However, it doesn't connect to sibling tools (e.g., how to use the URL with ratgeber_lesen) or mention potential pitfalls. It is complete for a search tool but misses cross-tool guidance.
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 bilingual descriptions and examples for all parameters. The description adds no extra parameter semantics beyond the schema, so 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 it searches the site's guides on specific topics (air conditioning, window sealing, etc.) and returns title, URL, and summary. This distinguishes it from sibling tools like ratgeber_lesen (which reads a guide) and the various 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 description implies use for finding citable sources related to the listed topics. It gives a clear use case but doesn't explicitly exclude other contexts or mention when to prefer alternatives. A note on using this before ratgeber_lesen would make it stronger.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Taupunkt der Außenluft und die Antwort auf 'darf ich jetzt lüften?'. — Dew point of the outside air and whether opening the window right now would make a basement or damp room wetter (Magnus formula, walls counted 2 °C below room temperature).
| Name | Required | Description | Default |
|---|---|---|---|
| innen_temp_c | Yes | Innen-/Kellertemperatur in °C (Wände werden 2 °C kühler gerechnet) | |
| aussen_temp_c | Yes | Außentemperatur in °C | |
| aussen_luftfeuchte_prozent | Yes | Relative Luftfeuchte außen in % (5–100) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses the use of the Magnus formula and the assumption that walls are 2°C cooler than room temperature, giving insight into the calculation. It also hints at the output being an answer (yes/no) to the venting question.
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 succinct sentences (one German, one English) pack the essential purpose, method, and key assumption without waste. Every word 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?
Given no output schema and no annotations, the description covers the core functionality, input context, and calculation method. It could be slightly more explicit about the exact return format (boolean vs. numeric), but the 'Antwort' wording implies a clear answer.
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 goes beyond by explaining the relevance of the inner temperature parameter (walls counted 2°C below) and the formula used, adding meaningful context to the inputs.
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: computing the dew point of outside air and answering whether opening a window would make a basement or damp room wetter. It is specific and distinct from sibling tools which cover heating, cooling, and guide queries.
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 when one wants to know if venting is safe for a basement/damp room. No explicit exclusions or alternatives are mentioned, but the context is clear enough for the intended scenario.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
Superseded by io.github.f-tiger/hvac-btu-heat-klimaanlage — same server, same URL.
Superseded by io.github.f-tiger/hvac-btu-heat-klimaanlage — same server, same URL.
US plug-in solar (balcony solar) law registry with a daily UL 3700 certification watch.
Heat pump vs gas hot water (AU): rebate stack, net outlay and payback verdict by postcode.
Add one secure layer between your agents and this server.
Each tool targets a distinct calculation or data source: subsidies, BTU sizing, seal length, heating power, heatwave forecast, running cost, guide search/read, and dew point. No two tools have overlapping purposes, so an agent can easily select the right one.
All tool names use lowercase with underscores and German terms, but the semantic pattern is slightly mixed: most follow 'topic_noun' (e.g., btu_empfehlung), while two are 'object_verb' (ratgeber_lesen, ratgeber_suche). This is a minor deviation from a fully uniform convention.
Nine tools is well within the optimal 3-15 range for a niche advice server. Each tool addresses a specific aspect of HVAC/energy guidance, and the inclusion of guide search/read does not make the set feel bloated.
The server covers the core domain well: cooling and heating sizing, running costs, window sealing, ventilation, and heatwave data. It lacks dedicated tools for dehumidifier sizing or AC comparison, but these topics are accessible through the guide search/read tools, so agents can work around the gaps.