AgentEuro

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

No annotations are provided, so the description bears full weight. It states 'Returns live rates' implying a non-destructive read operation, but does not disclose caching, update frequency, auth requirements, or any other behavioral traits beyond the basic read nature.

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

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

Two concise sentences front-load the primary functionality (exchange rates vs interest rates) and specify the data source. No redundant information or unnecessary 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?

The tool has no output schema, so the description should compensate. It notes 'Returns live rates' but does not describe the return format or structure, leaving ambiguity about exactly what agents will receive. For a simple tool, this is adequate but not fully complete.

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

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

Schema coverage is 100%, so baseline is 3. The description enriches the schema by specifying that rates are 'for EUR against major currencies' and that interest rates are 'ECB key interest rates (ESTR)', adding value beyond the 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 verb 'Get' and the resources 'ECB exchange rates' and 'ECB key interest rates (ESTR)', distinguishing it from sibling tools like get_eurostat_data and get_money_supply which cover different data sources.

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 lacks explicit guidance on when to use this tool versus its siblings, nor does it clarify when to choose fx vs rates. No usage context or 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?

No annotations are provided, so the description must disclose behavioral traits. It does not mention that the tool is read-only, whether rate limits apply, or what happens for invalid indicators. The description lacks detail on side effects or constraints.

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 concise sentences, front-loaded with the action and resource. No unnecessary words or repetition.

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

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

Given the lack of output schema and annotations, the description should explain return values or behavior for edge cases. It only lists supported indicators without specifying if other values are accepted. The output format is entirely unaddressed.

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 parameters are fully documented in the schema. The description adds minor value by specifying that inflation is HICP, but otherwise repeats schema content. 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 retrieves EU economic statistics from Eurostat and lists the covered indicators (GDP, inflation, unemployment, trade). It distinguishes from sibling tools like get_ecb_rates and get_money_supply, which focus on monetary policy and money supply, respectively.

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 no guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or context. An agent would have to infer usage from the tool name and sibling names.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It specifies that the tool is read-only (returns data) and limits output to the last 12 monthly observations. However, it does not mention any authentication requirements, rate limits, or potential side effects, leaving some behavioral aspects 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 extremely concise with just two sentences, each serving a distinct purpose: stating what the tool retrieves and specifying the output length. The most critical information is front-loaded ('Get ECB monetary data'), making it easy for an AI agent to quickly grasp the tool's function.

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

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

Given the tool's simplicity (one parameter, no output schema), the description covers the essential purpose and output details. It specifies the return format (last 12 monthly observations) and the two data series options. It is sufficiently complete for an agent to decide when to use this tool, though it could mention potential error cases or data formatting.

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% with a clear description of the series parameter choices. The tool description adds context by specifying that the data is for the Euro Area and includes both M3 and TARGET2, but this largely duplicates the schema. Since coverage is high, baseline is 3, and the description adds minimal extra meaning 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 retrieves ECB monetary data, specifically M3 money supply or TARGET2 balances, and returns the last 12 monthly observations. It uses specific verbs and resources ('Get ECB monetary data', 'M3 money supply', 'TARGET2 interbank balance data') and distinguishes itself from sibling tools like get_ecb_rates (which likely returns interest rates) and get_eurostat_data (which covers broader statistics).

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 this tool (for M3 or TARGET2 data) but does not explicitly provide when-not-to-use or mention alternatives. It states the two series options, which helps select the correct parameter, but lacks guidance on when to prefer this tool over siblings like get_ecb_rates or get_eurostat_data.

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