get_indicator_data

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

No annotations are provided, so the description carries the full burden. It states the tool 'gets' data, implying a read operation, and describes the return format (JSON with metadata, series, statistics). However, it does not disclose any potential side effects, authentication needs, or rate limits, which are important for a tool that fetches historical data.

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 with sections for Args, Returns, and Examples. Each sentence is informative and earns its place. It is slightly lengthy but still concise enough for an AI agent to parse quickly.

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 that an output schema exists (the agent can infer return structure), the description covers the essential aspects: purpose, required parameters, optional parameters, and example calls. It does not detail error handling or data limits, but it is sufficiently complete for a data retrieval 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?

The schema has 0% description coverage, so the description fully compensates by explaining each parameter with examples (e.g., indicator_id: '1293 for real demand, 549 for nuclear'; time_granularity: 'raw, hour, day, fifteen_minutes'). The examples provide concrete usage patterns, adding significant value beyond the raw 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 it retrieves time-series data for a specific electricity indicator from REE, listing examples like demand, generation, prices. It distinguishes itself from sibling tools that are more specialized (e.g., get_generation_mix, get_carbon_intensity) by being a generic fetcher for any indicator ID.

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 Args and examples showing how to use the tool, but does not explicitly state when to use this tool versus the more specific sibling tools. It lacks direct 'when-to-use' or 'when-not-to-use' guidance, leaving the agent to infer based on the generic nature.

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