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

With no annotations provided, the description carries the behavioral burden. It explains the alert levels and risk categories, which adds meaningful context. However, it does not disclose what happens when there is no alert, how current the data is, or whether the tool is read-only, leaving some behavior implicit.

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: it opens with the core purpose, then states when to use the tool, and ends with parameter guidance. Each sentence earns its place; there is no filler 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?

For a single-parameter tool with no output schema, the description covers the essential context: what the tool does, when to call it, and how to fill the parameter. It could add a small note about behavior when no alert exists, but overall it is sufficiently complete for the tool's simplicity.

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 provides only the parameter name 'comune' with no description, so schema coverage is 0%. The description fully compensates by explaining that comune is 'il nome esatto del comune' and gives a concrete example ('Gallarate'). This is precisely the guidance an agent needs to populate the parameter correctly.

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 resource ('Allerta meteo ufficiale della Protezione Civile') and a clear scope (per un comune italiano) with concrete alert types. It is immediately distinguishable from sibling weather tools like meteo_previsioni and meteo_adesso because it is specifically about official warnings, not forecasts or current conditions.

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 tells the agent when to use it: when the user asks about a weather alert, whether it is dangerous to go out, or about bad weather warnings. It does not mention alternative tools or exclusions, but for this simple one-parameter tool the usage context is clear enough.

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 must carry the behavioral burden. It clearly scopes the behavior to 'in questo momento' and Italian municipalities, and the read-only nature is evident from the query wording. It does not mention failure modes or output format, but for a simple weather lookup these are not critical.

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?

Three short sentences with no filler. The core function is front-loaded, usage triggers follow, and the only parameter is explained with examples. 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 single-parameter read-only weather tool, this is nearly complete. Absence of an output schema means the return shape is unspecified, but the description's phrasing ('tempo, temperatura') implies what the answer covers. No further context is essential.

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 0%; the only parameter 'citta' has just a type/title. The description compensates fully by explaining that it is a comune name and giving concrete examples ('Milano', 'Arsago Seprio'). This is exactly the meaning an agent needs.

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 clear statement of the tool's function: current weather ('Che tempo fa in questo momento') for an Italian location. This directly distinguishes it from sibling meteo_previsioni, which implies future weather, and from notizie/qualita_aria 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 gives explicit trigger examples ('cerca il tempo, la temperatura, se piove... adesso') so an agent knows when to select it. It does not explicitly name meteo_previsioni as the alternative for forecasts, but the repeated 'adesso' makes the boundary clear.

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 behavioral disclosure burden. It discloses the giorni range (1-7) and the Italian locality scope, which is helpful, but it does not describe the response format, data source, or error behavior. Overall, it adds some context but not rich behavioral detail.

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 three short sentences with no filler. The purpose is front-loaded, the usage guidance follows, and the key parameter constraint is stated clearly. 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 simple two-parameter weather forecast tool with no output schema and no annotations, this description is largely complete. It covers what the tool does, when to use it, and the main parameter constraint. It could mention the response format, but that is a minor omission for this use case.

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 0%, so the description must compensate. It adds meaning to citta as an Italian locality and constrains giorni to a 1-7 range, going beyond the bare schema type definitions. The default of 3 is left to the schema, which is acceptable.

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 provides weather forecasts for upcoming days in an Italian locality, which distinguishes it from the sibling tool meteo_adesso (current weather). It lacks an explicit verb like 'fornisce' or 'ottiene', but the resource and scope are unmistakable.

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 says to use the tool when the user asks about tomorrow, the weekend, or upcoming days. It does not explicitly name the alternative for current weather, but the sibling context and timeframe distinction make the usage boundary clear.

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, and it discloses the news source (ANSA), the headline-only scope, and the supported topics. However, it doesn't describe output structure, pagination, freshness, or whether any authentication is needed, so behavioral transparency is partial.

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 consists of three concise, front-loaded sentences: the core purpose, the usage condition, and the argument options. Every sentence adds value with no 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 tool with one optional parameter and no output schema, the description covers what it does, when to use it, and the valid values. It could add a bit more about the output nature (e.g., a list of headline strings) or the read-only nature, but the current information is largely sufficient for correct 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 description coverage and no enums, leaving 'argomento' as an opaque string. The description fully compensates by listing all available values: 'principali, cronaca, politica, economia, mondo, tecnologia, sport', which is essential for valid 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 returns 'Ultimi titoli delle notizie italiane dall'agenzia ANSA' (latest Italian news headlines from ANSA), identifying the resource and differentiating it from weather, alert, and reminder siblings. However, it lacks an explicit verb like 'gets' or 'lists', relying on the noun phrase and the subsequent usage instruction.

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 says 'Usalo quando l'utente chiede le notizie, che cosa succede o le ultime novita'' (use it when the user asks for news, what's happening, or latest updates), providing clear trigger conditions. It doesn't mention alternatives or exclusions, but the sibling tools are obviously different domains.

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 responsibility of explaining behavior. It does so by describing what is returned for each sezione value and clarifying the three distinct content categories. It doesn't explicitly discuss read-only guarantees, auth, or rate limits, but the operation is clearly a non-mutating retrieval, so this is a minor gap.

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 well-structured: purpose first, then usage guidance, then parameter mapping. Every sentence adds necessary information with no filler.

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 tool with one optional parameter, no required inputs, and no output schema, this description is complete enough for an agent to select and invoke it correctly. It covers the content, the user-intent triggers, and all parameter options.

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 provides only a parameter name and default, with zero description coverage. The tool description fully compensates by listing every accepted value for sezione and explaining exactly what each returns, making invocation unambiguous.

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's function: retrieving the latest articles from Elettronica In, Elettronica In PRO, or products from FuturaShop. It names the specific resource and distinguishes it from siblings like notizie_italia and meteo tools by focusing on this specific publication/shop domain.

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 explicit usage triggers: use it when the user asks for the latest news, articles, new magazine issues, or newly arrived products. It does not mention alternatives or exclusions, but the context is clear enough for an agent to know when this tool, rather than a general news or weather sibling, is appropriate.

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 of disclosing that this is a write operation, and it does state that it adds content. It does not describe side effects, persistence, or return behavior, but for a simple add tool the core mutation is clear.

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 with the action and intended user triggers front-loaded. Every clause earns its place; there is no redundant restating of the tool name.

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 add tool, the description adequately covers what it does and when to invoke it. It does not describe the expected response or any limitations, but there is no output schema and the operation is simple enough that this is a minor omission.

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 0% but there is only one required string parameter, 'testo'. The description maps it to the semantic content: the thing to remember or the shopping product. Although it does not name the parameter, the role is unambiguous from the tool's own sentence.

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 ('Aggiunge') and identifies the two resources: a thing to remember and a shopping-list item. This clearly distinguishes it from sibling tools promemoria_elenco and promemoria_rimuovi, which handle listing and removal.

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 gives explicit trigger conditions: use it when the user asks to remember something, jot something down, or add a product to the shopping list. It does not explicitly state exclusions or name alternatives, but the sibling names make the contrast obvious.

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. It clearly states this is a read operation ('Legge'), implying no side effects, and it names all data sources involved. It does not describe output format or ordering, but this is minor for a simple read 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 two compact sentences with no wasted words. The first sentence states the operation and resource; the second gives concrete usage triggers. The action is 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 zero-parameter, read-only list tool with no output schema, the description gives enough operational and usage context. It could mention the return format, but that is not essential for selecting and invoking such a simple 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 tool has zero parameters and the input schema fully documents that, so the baseline of 4 applies. No parameter explanation is needed 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 uses a specific action verb ('Legge' / reads) and identifies the exact resources: all things to remember and the shopping list. This clearly differentiates it from the add/remove reminder 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?

It explicitly states when to use the tool: when the user asks what to do, what they had noted down, or what is on the shopping list. It does not explicitly mention exclusions, but the trigger conditions are concrete and sufficient.

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 of disclosing behavior. It explicitly states that the tool removes an item, accepts a number or matching word, and that 'tutto' empties the entire list, which is an important destructive behavior. It does not discuss irreversibility or errors, but the core destructive semantics are 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 three short sentences with no filler. The main action is front-loaded, and the parameter behavior is explained immediately, making it easy for an agent to parse quickly.

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 single-parameter removal tool, the description covers the action, acceptable input forms, and the special 'tutto' command. The main gap is the absence of any return-value or error behavior, which matters somewhat because there is no output schema, but the agent still has enough to invoke 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?

The input schema has 0% description coverage, so the description must define the parameter. It explains that 'numero_o_testo' can be a list number, a word contained in the item, or the special sentinel 'tutto'. This adds significant meaning beyond the schema, though it could be even more precise about formats or edge cases.

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 ('Toglie') and names the affected resource ('lista dei promemoria o della spesa'), clearly identifying it as a removal operation. It also differentiates from the sibling tools by describing the remove action rather than add or list behavior.

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 makes the use case clear: when you need to remove an item from a reminders or shopping list. It does not explicitly point to alternatives like promemoria_aggiungi or promemoria_elenco, but the context and sibling names are obvious enough that an agent can route correctly.

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 and does disclose useful behavioral facts: only Italian locations are covered and the output contains an air-quality index with PM10/PM2.5. It does not mention update frequency, units, or behavior for unknown cities, so transparency is only partial.

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 tight sentences: the first states output and scope, the second states when to use it. No filler.

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 read tool with no output schema, the description covers purpose, output content, scope, and usage triggers. Minor gaps like units and error handling do not prevent correct 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?

Schema coverage is 0%, and the description only indirectly explains 'citta' by saying 'in una localita italiana'. That adds the key semantic constraint, but no format, examples, or explicit mapping from description to parameter.

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 the resource ('Qualita dell'aria in una localita italiana') and the specific data included (European index, PM10, PM2.5). It is clearly distinct from weather, news, alerts, and reminder siblings.

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 gives explicit usage triggers: user asks about air quality, smog, or whether they can go running. It does not explicitly name alternatives or state when not to use it, though those can be inferred from sibling names.

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