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

Annotations already cover readOnly, idempotent, and non-destructive behavior, so the description does not need to restate those. It adds meaningful context: the data source ('ECB Statistical Data Warehouse (data-api.ecb.europa.eu)'), that no auth is required, and the seasonal adjustment methodology. It does not mention rate limits or response details, but the output schema covers the response shape.

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: the first sentence states the core purpose, followed by a useful definitional sentence about M3, then filtering guidance, and finally source/auth info. Each sentence contributes meaningful information, though the M3 definition sentence is slightly encyclopedic rather than strictly operational.

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 query with three optional parameters and an output schema, the description is sufficient: it specifies the series content, frequency, filtering options, source, and auth requirements. The default behavior when no parameters are supplied is left to the schema, which already documents it, so nothing critical is missing.

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 each parameter already explained in detail, including precedence rules. The description only restates the filtering mechanism without adding new parameter-level semantics, so the baseline score of 3 applies.

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 a specific verb and resource: 'Get the euro area M3 monetary aggregate', with precise details on measurement (outstanding amount, working-day and seasonally adjusted, in EUR). This unambiguously distinguishes it from sibling macro tools like HICP inflation or yield curve because M3 is a distinctly different indicator and the euro area scope is explicit.

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 practical usage guidance: it notes the series is monthly and that filtering is available via 'last_n_observations or start_period/end_period'. It does not explicitly name alternative tools or provide when-not-to-use exclusions, but the euro area M3 context is clear enough for an agent to route to this tool.

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