Live Grid Data

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds behavioral context beyond annotations: data comes from EIA hourly RTO telemetry, is raw real-time telemetry, is scoped to one ISO per call, and covers a 24h demand curve. This helps set expectations about freshness and granularity.

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 dense but every clause earns its place: it states the data scope, source, acceptable values, and key exclusions with sibling pointers. The most important usage constraint is front-loaded, and no filler or repetition exists.

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 rich input schema, output schema presence, and annotations covering safety and idempotency, the description provides the remaining necessary context: data source, ISO coverage, raw real-time nature, what not to use it for, and fallback tools. Nothing critical is missing for an agent to select and call this tool 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?

Schema description coverage is 100%, so the baseline is 3. The description adds value by clarifying that iso is effectively required ('Pass iso=PJM (any of the 7)') despite the schema showing 0 required parameters, and by giving concrete metric examples (fuel_mix, demand, demand_curve) and a period example (24h). This extra context helps the agent construct valid calls.

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 names a specific verb-resource pair: retrieving real-time electricity grid data for seven named US ISOs, with explicit data types (fuel mix, demand, 24h demand curve). It clearly differentiates from sibling tools like get_grid_intelligence and get_energy_prices.

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 when-to-use guidance: use for real-time ISO telemetry, and explicitly says not to use for power-availability, time-to-power, interconnection-queue, or retail/gas price analysis, naming get_grid_intelligence and get_energy_prices as alternatives. It also routes non-US grids to get_grid_scoreboard.

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