energy_carbon_eu

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive, so the bar is lower. The description adds the source URL and return type (pandas.DataFrame), which is useful, but it doesn't disclose details like data granularity, date range, or update frequency beyond 'daily'.

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: a title, a source URL, and a return type. Every line adds value, no filler. This is a model of efficiency for a simple parameterless 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 no parameters and no output schema, the description covers the essentials: source and return type. However, the naming inconsistency (EU vs. international) could mislead an agent, and the description doesn't explain what columns or metrics are included in the DataFrame, slightly reducing completeness for a data-returning tool.

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?

There are zero parameters, so the baseline of 4 applies. The description correctly documents the return type, and since there are no parameters to explain, nothing further is needed.

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 returns '国际碳情每日行情数据' (international carbon daily market data) from the Shenzhen Carbon Exchange, which is a specific resource. However, the tool name says 'eu' but the description says 'international', creating slight ambiguity about regional scope, so it's not a perfect 5.

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?

There is no guidance on when to use this tool versus the many sibling tools like energy_carbon_domestic, energy_carbon_bj, etc. No exclusions or alternatives are mentioned, leaving the agent to guess which carbon data source to pick.

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