get_gas_intelligence

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds valuable behavioral context: it returns data with per-field provenance, never fabricates values, surfaces nulls honestly, and notes deliberate omissions. This goes beyond the annotations without contradicting them.

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 front-loaded with the primary use case and then organizes details about parameters and returns. It is somewhat lengthy but every sentence serves a purpose (examples, caveats, alternatives). Minor redundancy (e.g., listing fields that could be inferred) prevents a 5, but it remains well-structured and informative.

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?

The tool has no output schema and returns a complex object with many fields, data-basis tags, and special behaviors (null handling, omissions). The description fully documents the return structure, explains every field's meaning, and addresses edge cases like sparse tariff tables. It is complete enough for an agent to understand the tool's output and use it 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% (both 'state' and 'region' have descriptions). The description repeats the parameter expectations with examples ('TX', 'Texas', 'Virginia') but does not add deeper semantics beyond what the schema already provides. A baseline of 3 is appropriate when schema fully covers 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 starts with a specific use case: 'Use when a human asks about gas-fired or behind-the-meter power economics for a data center in a US state'. It clearly distinguishes itself from sibling tools by stating it is the 'GAS analogue of get_grid_intelligence' and explicitly says not to use it for electricity grid headroom or the DCGI score alone, referencing sibling names. This makes the purpose very clear and differentiated.

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 when-to-use (human asks about gas-fired/BTM economics) and when-not-to-use (electricity grid headroom, DCGI score alone) guidance, directly naming alternative tools (get_grid_intelligence, get_gas_index). It also lists what is deliberately omitted (gas storage, LNG, firm pipeline capacity), giving the agent clear boundaries on appropriate contexts.

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