EkoPump

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds useful behavioral context about aggregating from multiple official sources and clearly explains that the source/severity breakdown reflects the total real count, not just the sampled alerts.

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 plus a clearly formatted Args section. Information is front-loaded with the main purpose, and no words are wasted.

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 one-parameter, read-only tool with no output schema, the description adequately covers what the tool returns (breakdown by source/severity and a sample of individual alerts), the sources involved, and the limit behavior. Annotations handle safety context.

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

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

The schema fully describes the limit parameter with range, default, and the note about the breakdown always reflecting the total. The description repeats this information without adding new semantic details, so it aligns with the baseline for high schema 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 returns currently active alerts aggregated from multiple official sources, listing specific source types (DGT, EONET/EFFIS, MeteoAlarm, NWS). This distinguishes it from sibling tools that focus on stations, coverage, and fuel prices.

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 usage context is implied by the description—this tool is for retrieving active alerts—but there is no explicit guidance on when to use it versus the sibling tools or when not to use it. No alternatives are mentioned.

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?

Annotations already declare readOnly, idempotent, and non-destructive hints. The description adds useful behavioral context: the error behavior when a city is not recognized, the default and range for limit, and the ordering of results. This goes beyond annotations without contradicting them.

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

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

The description is concise and well-structured: a clear purpose sentence followed by parameter explanations in a standard args format. Every sentence adds value, and the front-loaded purpose ensures quick understanding.

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 read-only tool with no output schema, the description adequately explains what is returned (stations, prices for gasoline and diesel, ordering). It does not specify exact field names, but given the tool's simplicity and good annotations, this is sufficient.

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 schema already documents both parameters well. The description repeats this information but adds the detail that invalid city errors include valid examples, which is not in the schema. This is a small increment, 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 the tool returns gas stations with the best prices in a specific city, ordered from cheapest to most expensive, including gasoline and diesel prices. This is a specific verb+resource combination that distinguishes it from siblings like 'get_country_coverage' and 'get_active_alerts'.

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 clearly implies when to use it (for city-specific fuel price queries) and mentions the city must be covered by EkoPump. However, it does not explicitly state when not to use it or mention alternative tools for broader coverage, though sibling names make this reasonably 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?

Annotations already declare readOnly, idempotent, and non-destructive, and the description adds context by explaining the aggregated summary behavior when country is omitted. It also enumerates the data types covered, clarifying what 'coverage' means without contradicting annotations.

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 plus an Args block, with the core function stated upfront. The Args block is somewhat redundant with the schema, but the overall length is appropriate and contains 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?

There is no output schema, so the description carries the burden of explaining return behavior. It states it returns the types of live data available per country, which is sufficient for a simple lookup tool, though the exact return format (e.g., list of strings) is not specified.

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 fully describes the country parameter (ISO code, optional, omitted behavior) with 100% coverage, providing a baseline of 3. The description's Args section repeats this but adds an example ('PT'), offering a minor enhancement beyond the 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 returns live data coverage types per country (or all countries), listing eight data categories (e.g., combustible, transporte público). It distinguishes from sibling tools that focus on specific data types by being a coverage overview for a country. The verb 'Devuelve' plus resource makes the purpose unambiguous.

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 explicit use cases, such as answering whether EkoPump has data for a country or what it covers in Portugal. It explains the optional country parameter's behavior (aggregate summary when omitted). However, it does not directly state when not to use this tool versus 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?

Beyond the annotations (read-only, idempotent, non-destructive), the description adds valuable context: it states that data is aggregated from official sources by EkoPump, that no authentication is required, and that prices are returned in euros per liter unless originally in another currency (already converted). These are non-obvious behavioral details.

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

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

The description is concise and front-loaded with the core functionality. The Args section is somewhat redundant with the schema, but it is clearly formatted and every sentence contributes useful information (source, auth, return format).

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 read-only tool with one optional parameter and no output schema, the description explains the return value scope (per country or all countries), the fuel types and Brent, and the currency/unit of prices. It is sufficient for an agent to invoke and interpret results 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 covers 100% of the parameter description, including the ISO format and the omission behavior. The description's Args section repeats the same information without adding new semantics beyond what the schema already provides, so the baseline score of 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 uses a specific verb ('Devuelve') and clearly names the resource: national average prices for gasoline (G95), G98, and diesel per country, plus Brent oil price. This resource is distinct from sibling tools (alerts, city stations, country coverage), making it easy to distinguish.

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 its usage by describing it as a read-only data source, but it does not explicitly state when to use this tool over alternatives. It lacks any mention of alternative tools or exclusions, so the context is only implicit.

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