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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=true, so the description does not need to repeat safety. It adds that the tool returns 'an array of {date, value}', which clarifies the return format. It also provides example series IDs and their meanings (e.g., EUR/SEK fixing, policy rate). It does not mention any limits, pagination, or error behavior, but the annotations cover the core behavioral traits. The description adds value beyond 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 three sentences: purpose, parameter details, and example plus hint. Every sentence earns its place. It is front-loaded with the core function, then adds specifics. No filler. The structure is optimal for quick parsing by an AI agent.

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 (3 required parameters, no output schema, read-only), the description covers all essential aspects: what it does, parameters with format, return shape, examples, and a pointer to a sibling for discovery. No empty spaces remain that would hinder correct invocation. The description is self-contained and actionable.

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 meaning by specifying date format (YYYY-MM-DD), providing real series ID examples (SEKEURPMI, SECBREPOEFF) with their economic context, and showcasing a use case. It also includes an example JSON snippet in the schema. This enriches the agent's understanding beyond the schema's property 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 resource 'time-series observations for one series over a date range'. It specifies parameters (series ID, from/to) and includes concrete examples (SEKEURPMI, SECBREPOEFF). It distinguishes from sibling tool list_series by advising its use for series discovery, and implicitly from latest_observation by focusing on a date range. The purpose is unambiguous and actionable.

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 explains when to use this tool: for a single series over a date range. It mentions companion tool list_series for discovering IDs. However, it does not explicitly state when NOT to use it (e.g., for multiple series or latest observation only). There are siblings like latest_observation and cross_rates that could serve similar needs, but no direct comparison is given. The guidance is helpful but lacks exclusions.

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