get_gas_economics

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

Annotations declare readOnlyHint: true, and the description expands on this by detailing the returned object structure, including specific fields and their units. No contradictions. The description adds value beyond annotations by explaining what inputs produce what outputs.

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 with no wasted words. It front-loads the purpose and immediately provides actionable details, including input examples and output structure. It is dense but perfectly scoped.

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 output schema and low schema coverage, the description is complete: it explains inputs, outputs (including JSON shape), usage constraints, and even pairs with get_gas_index. The agent has all necessary information to invoke the 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?

Despite 0% schema description coverage, the description adds full meaning: it explains that 'market' is a slug for US data-center markets with examples, and 'heat_rate_btu_per_kwh' is for custom scenarios. This compensates completely for missing schema descriptions.

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 computes behind-the-meter gas-fired power economics for US data-center markets, listing specific outputs (e.g., Henry Hub spot, delivered tariffs, levelized cost). It distinguishes from siblings like get_gas_index and get_grid_data by explicitly stating what the tool is NOT for.

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 explicit guidance on when to use the tool ('the number a BTM developer compares against a grid PPA') and when not to use it (for grid fuel mix or per-state gas suitability), naming alternative tools (get_grid_data, get_gas_index).

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