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

Beyond the readOnlyHint and idempotentHint annotations, the description adds a critical behavioral detail: future hours carry a forecast and a null actual, and it explicitly states 'never a fake 0'. This prevents a common misinterpretation of missing data. It also scopes the data to 'today' and mentions the source feed.

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 two sentences: the first delivers the core purpose and data characteristics, the second covers data behavior and example use cases. Every sentence earns its place, with no redundant filler.

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 zero-parameter, read-only tool with no output schema, the description covers purpose, source, units (MW), forecast vs actual distinction, null handling for future hours, and typical use cases. It gives an agent everything needed to invoke and interpret the result correctly.

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 tool has zero parameters and the schema is empty with 100% coverage, so no parameter documentation is needed. The description still adds semantic clarity about forecast vs actual values, which helps interpret the data even though it is not about parameters.

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 identifies the specific resource (MISO wind and solar generation for today), the data type (hourly forecast vs actual output in MW), and the source. This is enough to distinguish it from sibling MISO tools like fuel mix, LMP, load, and binding constraints without needing to open their schemas.

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 explicit example use cases: 'how accurate was the MISO wind forecast today', 'MISO solar output this afternoon', and renewable-curtailment/forecast-error questions. It does not explicitly name alternative tools to avoid, but the usage context is clear enough for an agent to decide when to call this tool.

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