validate_gas_gravities

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the logic (calculations based on inputs) and return format (tuple/dictionary), which is helpful. However, it lacks details on error handling, performance, or side effects (e.g., data mutation), leaving gaps for a tool that performs validation and imputation.

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 (DATA VALIDATION TOOL, Logic, Use Cases, Returns) and front-loaded key information. Every sentence earns its place by clarifying purpose, logic, usage, or output without redundancy, making it efficient and easy to parse.

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 tool's complexity (validation and imputation logic), no annotations, and an output schema (implied by 'Returns'), the description does a good job covering purpose, logic, and use cases. However, it could be more complete by addressing potential errors or assumptions in calculations, slightly reducing its adequacy for full contextual understanding.

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 description coverage is 0%, so the description must compensate. It adds value by explaining the 'request' parameter contains 'available gas gravities and GORs' and outlines the logic for handling sg_g and sg_sp, providing context beyond the bare schema. However, it does not detail all parameters (e.g., rst, rsp, sg_st), slightly limiting completeness.

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's purpose with specific verbs ('validate', 'impute', 'calculate') and resources ('gas gravities', 'missing values'), distinguishing it from siblings like 'gas_sg_from_composition' or 'weighted_average_gas_sg'. It explicitly defines what the tool does: checking consistency and calculating missing values based on provided inputs.

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 clear context for when to use this tool through listed 'Use Cases' (e.g., QC PVT data, fill gaps, validate measurements), which helps guide appropriate usage. However, it does not explicitly state when not to use it or name specific alternatives among siblings, keeping it from a perfect score.

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