petropt/petro-mcp

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the calculation but lacks critical behavioral details: it doesn't specify the output format (though an output schema exists), error conditions, numerical precision, or constraints on input ranges beyond the implied 0-1 for some parameters. For a computational tool with 8 parameters, this leaves significant gaps in understanding how the tool behaves in practice.

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 and efficient: a concise purpose statement followed by a parameter list with clear definitions. Every sentence earns its place, and there's no redundant information. It could be slightly improved by front-loading more context about the equation's applicability, but overall it's appropriately sized for the tool's complexity.

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 (8 parameters, computational nature) and the presence of an output schema, the description is partially complete. It thoroughly documents parameters but lacks usage guidelines and behavioral context. The output schema likely covers return values, reducing the need for that in the description, but for a tool with no annotations, more operational guidance would be beneficial to achieve full completeness.

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 description excels in parameter semantics, adding substantial value beyond the input schema. The schema has 0% description coverage (titles only like 'Rt', 'Phi'), but the description provides clear definitions, units, and ranges for all 8 parameters (e.g., 'True formation resistivity (ohm-m)', 'Porosity (fraction v/v, 0-1)'), including defaults for optional ones. This fully compensates for the schema's lack of documentation.

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: 'Calculate water saturation using Simandoux equation (shaly sands).' This specifies the verb ('calculate'), resource ('water saturation'), and method ('Simandoux equation'), distinguishing it from other saturation calculation tools like 'calculate_archie_sw' or 'calculate_indonesian_sw'. However, it doesn't explicitly differentiate when to use Simandoux over other saturation equations, which prevents a perfect score.

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 no guidance on when to use this tool versus alternatives. With sibling tools like 'calculate_archie_sw' and 'calculate_indonesian_sw' available, there's no mention of appropriate geological contexts, input data requirements, or comparative advantages. The agent must infer usage from the equation name alone, which is insufficient for informed tool selection.

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