get_yield_curve

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

With no annotations provided, the description carries the full burden. It discloses that data is 'updated daily' and specifies available maturities and issuer types, adding useful context. However, it does not mention potential rate limits, error conditions, or authentication needs, leaving gaps in behavioral understanding for a data retrieval 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 well-structured and front-loaded with core functionality, followed by details and examples. Each sentence adds value without redundancy, such as stating data source, maturity range, update frequency, and illustrative use cases, making it efficient and easy to parse.

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 no annotations and no output schema, the description does a good job covering the tool's purpose, parameters, and usage context. It could improve by detailing return format or error handling, but it adequately informs an agent for a data query tool with clear parameters and examples.

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 baseline is 3. The description adds value by specifying 'Available maturities range from 3 months to 30 years' and clarifying the data source and rating context, which enhances understanding beyond the schema's parameter descriptions, justifying a score above baseline.

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 'euro area government bond yield curve data from the ECB' and specifies it returns 'spot rates from the ECB's AAA-rated government bond yield curve.' This distinguishes it from sibling tools like get_exchange_rates or get_inflation by focusing on bond yields, with specific examples reinforcing the purpose.

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 usage through example questions like 'What is the current 10-year euro area yield?' but does not explicitly state when to use this tool versus alternatives such as get_interest_rates or search_datasets. It provides context for bond yield queries but lacks direct guidance on tool selection.

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