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. It discloses that the tool returns multiple outputs (net pay thickness, net-to-gross, average properties, per-sample pay flags), which adds valuable context beyond the input schema. However, it lacks details on error handling, performance, or data validation (e.g., array length matching), leaving behavioral gaps for a tool with 7 parameters.

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 efficiently structured: a purpose sentence, an output summary, and a parameter list. Each sentence earns its place by clarifying functionality or parameters without redundancy. It is front-loaded with the core purpose and 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 (7 parameters, no annotations, but with output schema), the description is largely complete. It explains inputs thoroughly and outputs generally, though it could benefit from more behavioral context (e.g., handling of invalid data). The output schema likely covers return values, reducing the need for detailed output explanation here.

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 0%, so the description must compensate fully. It provides detailed semantics for all 7 parameters, including units (ft, fraction v/v), meanings (e.g., 'porosity values at each depth'), and default values for cutoffs. This adds significant value beyond the bare schema, making parameters well-understood.

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: 'Determine net pay by applying porosity, Sw, and Vshale cutoffs to log data.' It specifies the verb ('determine'), resource ('net pay'), and method ('applying cutoffs to log data'), which distinguishes it from sibling tools focused on different calculations like economics, pressure, or saturation models.

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. It does not mention prerequisites, such as needing log data from specific sources, or compare it to similar tools like 'calculate_vshale' or 'calculate_effective_porosity' in the sibling list. Usage is implied only through the purpose statement.

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